Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Distributed-Systems-Guide
Distributed Systems Guide
https://github.com/mikeroyal/Distributed-Systems-Guide
- Apache Spark™ - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Spark Quick Start
- What is Apache Spark? | IBM
- Introduction to Apache Spark and Analytics | AWS
- Apache Spark 3.0: For Analytics & Machine Learning | NVIDIA
- .NET for Apache Spark™ | Big data analytics
- Apache Spark Basics | MATLAB & Simulink
- MATLAB Hadoop and Spark | MATLAB & Simulink
- Top Apache Spark Courses Online | Coursera
- Top Apache Spark Courses Online | Udemy
- Apache Spark In-Depth (Spark with Scala) | Udemy
- Learn Apache Spark with Online Courses | edX
- Apache Spark Essential Training Online Class | LinkedIn Learning
- Cloudera Developer Training for Apache Spark™ and Hadoop | Cloudera
- Databricks Certified Associate Developer for Apache Spark 3.0 certification | Databricks
- Apache Spark Training Courses | NobleProg
- Spark SQL
- Spark Streaming - tolerant stream processing engine built on the Spark SQL engine. It can express your streaming computation the same way you would express a batch computation on static data from various sources including [Apache Kafka](https://kafka.apache.org/), [Apache Flume](https://flume.apache.org/), and [Amazon Kinesis](https://aws.amazon.com/kinesis/).
- MLib - level optimization primitives and higher-level pipeline APIs.
- Graphx - parallel computation. At a high-level, GraphX extends the [Spark RDD](https://spark.apache.org/docs/latest/rdd-programming-guide.html) by introducing the Resilient Distributed Property Graph: a directed multigraph with properties attached to each vertex and edge.
- PySpark
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Koalas - docs/stable/reference/api/pandas.DataFrame.html) on top of [Apache Spark](https://spark.apache.org/).
- MLflow
- Tracking component
- Projects component
- Models component
- Model Registry
- Apache PredictionIO
- Cluster Manager for Apache Kafka(CMAK)
- BigDL
- Apache Cassandra™ - tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.
- Apache Flume
- Apache Mesos
- Apache HBase™ - source, NoSQL, distributed big data store. It enables random, strictly consistent, real-time access to petabytes of data. HBase is very effective for handling large, sparse datasets. HBase serves as a direct input and output to the Apache MapReduce framework for Hadoop, and works with Apache Phoenix to enable SQL-like queries over HBase tables.
- Hadoop Distributed File System (HDFS) - yarn/hadoop-yarn-site/YARN.html).
- Apache Arrow - independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Neo4j - strength graph database that combines native graph storage, advanced security, scalable speed-optimized architecture, and ACID compliance to ensure predictability and integrity of relationship-based queries.
- ElasticSearch - capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
- Logstash
- Kibana
- Trino - us/azure/architecture/data-guide/relational-data/etl), allow them all to use standard SQL statement, and work with numerous data sources and targets all in the same system.
- Extract, transform, and load (ETL)
- Redis(REmote DIctionary Server) - memory data structure store, used as a database, cache, and message broker. It provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
- Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Apache MXNet
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- Anaconda
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Scikit-Learn
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- SQL
- NoSQL - SQL" to highlight that the database can handle huge volumes of rapidly changing, unstructured data in different ways than a relational (SQL-based) database with rows and tables.
- Transact-SQL(T-SQL) - SQL commands.
- Introduction to Transact-SQL
- SQL Tutorial by W3Schools
- Learn SQL Skills Online from Coursera
- SQL Courses Online from Udemy
- SQL Online Training Courses from LinkedIn Learning
- Learn SQL For Free from Codecademy
- GitLab's SQL Style Guide
- OracleDB SQL Style Guide Basics
- Tableau CRM: BI Software and Tools
- Databases on AWS
- Best Practices and Recommendations for SQL Server Clustering in AWS EC2.
- Connecting from Google Kubernetes Engine to a Cloud SQL instance.
- Educational Microsoft Azure SQL resources
- MySQL Certifications
- SQL vs. NoSQL Databases: What's the Difference?
- What is NoSQL?
- Netdata - fidelity infrastructure monitoring and troubleshooting, real-time monitoring Agent collects thousands of metrics from systems, hardware, containers, and applications with zero configuration. It runs permanently on all your physical/virtual servers, containers, cloud deployments, and edge/IoT devices, and is perfectly safe to install on your systems mid-incident without any preparation.
- Azure Data Studio
- Azure SQL Database - powered and automated features that optimize performance and durability for you. Serverless compute and Hyperscale storage options automatically scale resources on demand, so you can focus on building new applications without worrying about storage size or resource management.
- Azure SQL Managed Instance - premises applications to the cloud with very few application and database changes. Managed instance has split compute and storage components.
- Azure Synapse Analytics
- MSSQL for Visual Studio Code
- SQL Server Data Tools (SSDT)
- Bulk Copy Program - line tool that comes with Microsoft SQL Server. BCP, allows you to import and export large amounts of data in and out of SQL Server databases quickly snd efficeiently.
- SQL Server Migration Assistant
- SQL Server Integration Services - level data integration and data transformations solutions. Use Integration Services to solve complex business problems by copying or downloading files, loading data warehouses, cleansing and mining data, and managing SQL Server objects and data.
- SQL Server Business Intelligence(BI)
- Tableau - releases/press-release-details/2019/Salesforce-Completes-Acquisition-of-Tableau/default.aspx).
- DataGrip - sensitive code completion, helping you to write SQL code faster. Completion is aware of the tables structure, foreign keys, and even database objects created in code you're editing.
- RStudio - highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
- MySQL - native applications using the world's most popular open source database.
- PostgreSQL - relational database system with over 30 years of active development that has earned it a strong reputation for reliability, feature robustness, and performance.
- Amazon DynamoDB - value and document database that delivers single-digit millisecond performance at any scale. It is a fully managed, multiregion, multimaster, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications.
- Apache Cassandra™ - tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.
- Apache HBase™ - source, NoSQL, distributed big data store. It enables random, strictly consistent, real-time access to petabytes of data. HBase is very effective for handling large, sparse datasets. HBase serves as a direct input and output to the Apache MapReduce framework for Hadoop, and works with Apache Phoenix to enable SQL-like queries over HBase tables.
- Hadoop Distributed File System (HDFS) - yarn/hadoop-yarn-site/YARN.html).
- Apache Mesos
- Apache Spark - in modules for streaming, SQL, machine learning and graph processing.
- ElasticSearch - capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
- Logstash
- Kibana
- Trino - us/azure/architecture/data-guide/relational-data/etl), allow them all to use standard SQL statement, and work with numerous data sources and targets all in the same system.
- Extract, transform, and load (ETL)
- Redis(REmote DIctionary Server) - memory data structure store, used as a database, cache, and message broker. It provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
- FoundationDB - value store and employs ACID transactions for all operations. It is especially well-suited for read/write workloads but also has excellent performance for write-intensive workloads. FoundationDB was acquired by [Apple in 2015](https://techcrunch.com/2015/03/24/apple-acquires-durable-database-company-foundationdb/).
- IBM DB2 - empowered capabilities designed to help you manage both structured and unstructured data on premises as well as in private and public cloud environments. Db2 is built on an intelligent common SQL engine designed for scalability and flexibility.
- MongoDB - like documents.
- OracleDB - critical data with the highest availability, reliability, and security.
- MariaDB - critical applications.
- SQLite - language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine.SQLite is the most used database engine in the world. SQLite is built into all mobile phones and most computers and comes bundled inside countless other applications that people use every day.
- SQLite Database Browser
- InfluxDB - us/azure/architecture/data-guide/relational-data/etl) or monitoring and alerting purposes, user dashboards, Internet of Things sensor data, and visualizing and exploring the data and more. It also has support for processing data from [Graphite](http://graphiteapp.org/).
- Atlas - memory dimensional [time series database](https://en.wikipedia.org/wiki/Time_series_database).
- CouchbaseDB - model NoSQL document-oriented database](https://en.wikipedia.org/wiki/Multi-model_database). It creates a key-value store with managed cache for sub-millisecond data operations, with purpose-built indexers for efficient queries and a powerful query engine for executing SQL queries.
- dbWatch - premise, hybrid/cloud database environments.
- Cosmos DB Profiler - time visual debugger allowing a development team to gain valuable insight and perspective into their usage of Cosmos DB database. It identifies over a dozen suspicious behaviors from your application’s interaction with Cosmos DB.
- Adminer
- DBeaver
- DbVisualizer
- AppDynamics Database - Volume Production Environment.
- Toad - in expertise. This SQL management tool resolve issues, manage change and promote the highest levels of code quality for both relational and non-relational databases.
- Lepide SQL Server - to-use, graphical user interface.
- Sequel Pro
- AWS Certified Security - Specialty Certification
- Microsoft Certified: Azure Security Engineer Associate
- Google Cloud Certified Professional Cloud Security Engineer
- Cisco Security Certifications
- The Red Hat Certified Specialist in Security: Linux
- Linux Professional Institute LPIC-3 Enterprise Security Certification
- Cybersecurity Training and Courses from IBM Skills
- Cybersecurity Courses and Certifications by Offensive Security
- Citrix Certified Associate – Networking(CCA-N)
- Citrix Certified Professional – Virtualization(CCP-V)
- CCNP Routing and Switching
- Certified Information Security Manager(CISM)
- Wireshark Certified Network Analyst (WCNA)
- Juniper Networks Certification Program Enterprise (JNCP)
- Networking courses and specializations from Coursera
- Network & Security Courses from Udemy
- Network & Security Courses from edX
- cURL - line tool for transferring data using various network protocols(HTTP, HTTPS, FTP, FTPS, SCP, SFTP, TFTP, DICT, TELNET, LDAP LDAPS, MQTT, POP3, POP3S, RTMP, RTMPS, RTSP, SCP, SFTP, SMB, SMBS, SMTP or SMTPS). cURL is also used in cars, television sets, routers, printers, audio equipment, mobile phones, tablets, settop boxes, media players and is the Internet transfer engine for thousands of software applications in over ten billion installations.
- cURL Fuzzer
- DoH - alone application for DoH (DNS-over-HTTPS) name resolves and lookups.
- HTTPie - line HTTP client. Its goal is to make CLI interaction with web services as human-friendly as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.
- HTTPStat
- Wuzz
- Websocat - line client for WebSockets, like netcat (or curl) for ws:// with advanced socat-like functions.
- JSON Web Token (JWT) - safe means of representing claims to be transferred between two parties. The claims in a JWT are encoded as a JSON object that is digitally signed using JSON Web Signature (JWS).
- OAuth 2.0 - party applications to access the user account.
- HVM (Hardware Virtual Machine) - metal hardware.
- PV(ParaVirtualization) - assisted virtualization.
- KVM (for Kernel-based Virtual Machine) - V). It consists of a loadable kernel module, kvm.ko, that provides the core virtualization infrastructure and a processor specific module, kvm-intel.ko or kvm-amd.ko.
- QEMU
- Hyper-V
- VirtManager
- oVirt - source distributed virtualization solution, designed to manage your entire enterprise infrastructure. oVirt uses the trusted KVM hypervisor and is built upon several other community projects, including libvirt, Gluster, PatternFly, and Ansible.Founded by Red Hat as a community project on which Red Hat Enterprise Virtualization is based allowing for centralized management of virtual machines, compute, storage and networking resources, from an easy-to-use web-based front-end with platform independent access.
- HyperKit - level components such as the [VPNKit](https://github.com/moby/vpnkit) and [DataKit](https://github.com/moby/datakit). HyperKit currently only supports macOS using the [Hypervisor.framework](https://developer.apple.com/library/mac/documentation/DriversKernelHardware/Reference/Hypervisor/index.html) making it a core component of Docker Desktop for Mac.
- Intel® Graphics Virtualization Technology (Intel® GVT) - through, starting from 4th generation Intel Core (TM) processors with Intel processor graphics(Broadwell and newer). It can be used to virtualize the GPU for multiple guest virtual machines, effectively providing near-native graphics performance in the virtual machine and still letting your host use the virtualized GPU normally.
- Apple Hypervisor - party kernel extensions. Hypervisor provides C APIs so you can interact with virtualization technologies in user space, without writing kernel extensions (KEXTs). As a result, the apps you create using this framework are suitable for distribution on the [Mac App Store](https://www.appstore.com/).
- Apple Virtualization Framework - level APIs for creating and managing virtual machines on Apple silicon and Intel-based Mac computers. This framework is used to boot and run a Linux-based operating system in a custom environment that you define. It also supports the [Virtio specification](https://www.redhat.com/en/virtio-networking-series), which defines standard interfaces for many device types, including network, socket, serial port, storage, entropy, and memory-balloon devices.
- Apple Paravirtualized Graphics Framework - accelerated graphics for macOS running in a virtual machine, hereafter known as the guest. The operating system provides a graphics driver that runs inside the guest, communicating with the framework in the host operating system to take advantage of Metal-accelerated graphics.
- Cloud Hypervisor - lang.org/) and is based on the [rust-vmm](https://github.com/rust-vmm) crates.
- VMware vSphere Hypervisor - metal hypervisor that virtualizes servers; allowing you to consolidate your applications while saving time and money managing your IT infrastructure.
- Xen
- Ganeti
- Packer
- Vagrant - to-use workflow and focus on automation, Vagrant lowers development environment setup time, increases production parity, and makes the "works on my machine" excuse a relic of the past. It provides easy to configure, reproducible, and portable work environments built on top of industry-standard technology and controlled by a single consistent workflow to help maximize the productivity and flexibility of you and your team.
- Parallels Desktop - unleashes-m1/)) and ChromeOS.
- VMware Fusion - cluster) to developers, and IT professionals on the Mac.
- VMware Workstation
- NAS (Network Attached Storage)
- GlusterFS - the-shelf hardware, you can create large, distributed storage solutions for media streaming, data analysis, and other data- and bandwidth-intensive tasks.
- Ceph - defined storage solution designed to address the object, block, and file storage needs of data centers adopting open source as the new norm for high-growth block storage, object stores and data lakes. Ceph provides enterprise scalable storage while keeping [CAPEX](https://corporatefinanceinstitute.com/resources/knowledge/modeling/how-to-calculate-capex-formula/) and [OPEX](https://www.investopedia.com/terms/o/operating_expense.asp) costs in line with underlying bulk commodity disk prices.
- ZFS - ready open source file system and volume manager with unprecedented flexibility and an uncompromising commitment to data integrity.
- OpenZFS - source storage platform. It includes the functionality of both traditional file systems and volume manager. It has many advanced features including:
- Btrfs
- Apple File System (APFS)
- NTFS(New Technology File System)
- exFAT(Extended File Allocation Table )
- VMware
- VMware
- HPE(Hewlett Packard Enterprise) Telco Blueprints overview
- Network Functions Virtualization Infrastructure (NFVI) by Cisco
- Introduction to vCloud NFV Telco Edge from VMware
- VMware Telco Cloud Automation(TCA) Architecture Overview
- 5G Telco Cloud from VMware
- Maturing OpenStack Together To Solve Telco Needs from Red Hat
- Red Hat telco ecosystem program
- OpenStack for Telcos by Canonical
- Open source NFV platform for 5G from Ubuntu
- Understanding 5G Technology from Verizon
- Verizon and Unity partner to enable 5G & MEC gaming and enterprise applications
- Understanding 5G Technology from Intel
- Understanding 5G Technology from Qualcomm
- Telco Acceleration with Xilinx
- VIMs on OSM Public Wiki
- Amazon EC2 Overview and Networking Introduction for Telecom Companies
- Citrix Certified Associate – Networking(CCA-N)
- Citrix Certified Professional – Virtualization(CCP-V)
- CCNP Routing and Switching
- Certified Information Security Manager(CISM)
- Wireshark Certified Network Analyst (WCNA)
- Juniper Networks Certification Program Enterprise (JNCP)
- Cloud Native Computing Foundation Training and Certification Program
- Open Stack - as-a-service (IaaS) to orchestrate data center operations on bare metal, private cloud hardware, public cloud resources, or both (hybrid/multi-cloud architecture). OpenStack includes advance use of virtualization & SDN for network traffic optimization to handle the core cloud-computing services of compute, networking, storage, identity, and image services.
- StarlingX - low latency use cases.
- Airship
- Network functions virtualization (NFV)
- Software Defined Networking (SDN) - based controllers or application programming interfaces (APIs) to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (routers and switches) to control network traffic.
- Virtualized Infrastructure Manager (VIM)
- Management and Orchestration(MANO) - hosted initiative to develop an Open Source NFV Management and Orchestration (MANO) software stack aligned with ETSI NFV. Two of the key components of the ETSI NFV architectural framework are the NFV Orchestrator and VNF Manager, known as NFV MANO.
- Magma - effective and extensible carrier-grade networks. Magma is 3GPP generation (2G, 3G, 4G or upcoming 5G networks) and access network agnostic (cellular or WiFi). It can flexibly support a radio access network with minimal development and deployment effort.
- OpenRAN - vendor deployments.
- Open vSwitch(OVS)
- Edge
- Multi-access edge computing (MEC) - parties across multi-vendor Multi-access Edge Computing platforms.
- Virtualized network functions(VNFs)
- Cloud-Native Network Functions(CNF)
- Physical Network Function(PNF)
- Network functions virtualization infrastructure(NFVI)
- Spring Boot - source micro framework maintained by Pivotal, which was acquired by VMware in 2019. It provides Java developers with a platform to get started with an auto configurable production-grade Spring application.
- Apache Mesos
- Apache Spark - in modules for streaming, SQL, machine learning and graph processing.
- Apache Hadoop - availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
- BOSH
- Maven
- Gradle - source build-automation system that builds upon the concepts of Apache Ant and Apache Maven and introduces a Groovy-based domain-specific language instead of the XML form used by Apache Maven for declaring the project configuration.
- Chef
- Puppet
- Ansible - source software provisioning, configuration management, and application-deployment tool. It runs on many Unix-like systems, and can configure both Unix-like systems as well as Microsoft Windows.
- Salt - based, open-source software for event-driven IT automation, remote task execution, and configuration management. Supporting the "Infrastructure as Code" approach to data center system and network deployment and management, configuration automation, SecOps orchestration, vulnerability remediation, and hybrid cloud control.
- Terraform - source infrastructure as code software tool created by HashiCorp.It enables users to define and provision a datacenter infrastructure using a high-level configuration language known as Hashicorp Configuration Language (HCL), or optionally JSON.
- Amazon web service(AWS) - to-use and cost-effective cloud computing solutions. The AWS platform is developed with a combination of infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service (SaaS) offerings.
- Microsoft Azure - managed data centers.
- Azure DevOps - platform, scalable apps and services; Azure Pipelines Continuously build, test, and deploy to any platform and cloud; Azure Lab Services Set up labs for classrooms, trials, development and testing, and other scenarios.
- Azure Draft - native applications on Kubernetes.
- Google Cloud Platform - leading tools(data management, hybrid & multi-cloud, and AI & ML) with Cloud Storage for enhanced support with everything from security and data transfer, to data backup and archive. Expand all . Backup, archival, and disaster recovery. Along with File systems and gateways.
- OpenStack - source software platform for cloud computing, mostly deployed as infrastructure-as-a-service that controls large pools of compute, storage, and networking resources throughout a datacenter, managed through a dashboard or via the OpenStack API. OpenStack works with popular enterprise and open source technologies making it ideal for heterogeneous infrastructure.
- Cloud Foundry
- Bamboo
- Drone - compose, to define and execute Pipelines inside Docker containers.
- Travis CI
- Circle CI
- Team City
- Shippable
- Spinnaker - cloud continuous delivery platform for releasing software changes with high velocity and confidence.
- Prow - ops via /foo style commands, and automatic PR merging. Prow has a microservice architecture implemented as a collection of container images that run as Kubernetes deployments.
- AWS ECS - performance container orchestration service that supports Docker containers and allows you to easily run and scale containerized applications on AWS. Amazon ECS eliminates the need for you to install and operate your own container orchestration software, manage and scale a cluster of virtual machines, or schedule containers on those virtual machines.
- AWS CodeBuild
- CFEngine - source configuration management system, written by Mark Burgess.Its primary function is to provide automated configuration and maintenance of large-scale computer systems, including the unified management of servers, desktops, consumer and industrial devices, embedded networked devices, mobile smartphones, and tablet computers.
- Octpus Deploy - premises or in the cloud.
- AWS CodeDeploy - premises servers. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications.
- AWS Lambda - driven, serverless computing platform provided by Amazon as a part of the Amazon Web Services. It is a computing service that runs code in response to events and automatically manages the computing resources required by that code.
- Traefik - source Edge Router that makes publishing your services a fun and easy experience. It receives requests on behalf of your system and finds out which components are responsible for handling them. What sets Traefik apart, besides its many features, is that it automatically discovers the right configuration for your services.
- Kubernetes - source container-orchestration system for automating application deployment, scaling, and management. It was originally designed by Google, and is now maintained by the Cloud Native Computing Foundation.
- Google Kubernetes Engine (GKE) - ready environment for deploying containerized applications.
- OpenShift - term, enterprise support from one of the leading Kubernetes contributors and open source software companies.
- Rancher
- Docker - level virtualization to deliver software in packages called containers. Containers are isolated from one another and bundle their own software, libraries and configuration files; they can communicate with each other through well-defined channels. All containers are run by a single operating-system kernel and are thus more lightweight than virtual machines.
- Rook - native storage orchestrator for Kubernetes that turns distributed storage systems into self-managing, self-scaling, self-healing storage services. It automates the tasks of a storage administrator: deployment, bootstrapping, configuration, provisioning, scaling, upgrading, migration, disaster recovery, monitoring, and resource management.
- Rkt - native container engine for Linux. It is composable, secure, and built on standards.
- Open Container Initiative
- Buildah
- Podman
- Containerd - level storage to network attachments and beyond. It is available for Linux and Windows.
- Containerd.io
- CNCF Cloud Native Interactive Landscape
- Build Cloud-Native applications in Microsoft Azure
- Cloud-Native application development for Google Cloud
- Cloud-Native development for Amazon Web Services
- Cloud Native Computing Foundation Training and Certification Program
- Cloud Foundry Developer Training and Certification Program
- Cloud-Native Architecture Course on Pluralsight
- AWS Fundamentals: Going Cloud-Native on Coursera
- Developing Cloud-Native Apps w/ Microservices Architectures course on Udemy
- How load balancing works for cloud native applications with Azure Application Gateway on Linkedin Learning
- Developing Cloud Native Applications course on edX
- Cloud Native courses from IBM
- Machine Learning
- Machine Learning by Stanford University from Coursera
- AWS Training and Certification for Machine Learning (ML) Courses
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure AI Engineer Associate
- Azure Machine Learning training and deployment
- Learning Machine learning and artificial intelligence from Google Cloud Training
- Machine Learning Crash Course for Google Cloud
- JupyterLab
- Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
- How to run Jupyter Notebooks in your Azure Machine Learning workspace
- Machine Learning Courses Online from Udemy
- Machine Learning Courses Online from Coursera
- Learn Machine Learning with Online Courses and Classes from edX
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- Keras - level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
- PyTorch
- Amazon SageMaker
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Microsoft Cognitive Toolkit (CNTK) - source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
- Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Apache MXNet
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- Anaconda
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Scikit-Learn
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- nGraph - of-use to AI developers.
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Apache PredictionIO
- Cluster Manager for Apache Kafka(CMAK)
- BigDL
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- Tensorman
- Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
- Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
- XGBoost
- cuML - learn.
- Fuzzy logic - tree processing and better integration with rules-based programming.
- ResearchGate
- Support Vector Machine (SVM) - group classification problems.
- OpenClipArt
- Neural networks
- IBM
- Convolutional Neural Networks (R-CNN)
- CS231n
- Recurrent neural networks (RNNs)
- Slideteam
- Multilayer Perceptrons (MLPs) - layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.
- DeepAI
- Random forest - used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.
- wikimedia
- Decision trees - structured models for classification and regression.
- CMU
- Naive Bayes - theorem.html) with strong independence assumptions between the features.
- mathisfun
- Deep Learning - supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).
- Deep Learning Online Courses | NVIDIA
- Top Deep Learning Courses Online | Coursera
- Top Deep Learning Courses Online | Udemy
- Learn Deep Learning with Online Courses and Lessons | edX
- Deep Learning Online Course Nanodegree | Udacity
- Machine Learning Course by Andrew Ng | Coursera
- Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
- Data Science: Deep Learning and Neural Networks in Python | Udemy
- Understanding Machine Learning with Python | Pluralsight
- How to Think About Machine Learning Algorithms | Pluralsight
- Deep Learning Courses | Stanford Online
- Deep Learning - UW Professional & Continuing Education
- Deep Learning Online Courses | Harvard University
- Machine Learning for Everyone Courses | DataCamp
- Artificial Intelligence Expert Course: Platinum Edition | Udemy
- Top Artificial Intelligence Courses Online | Coursera
- Learn Artificial Intelligence with Online Courses and Lessons | edX
- Professional Certificate in Computer Science for Artificial Intelligence | edX
- Artificial Intelligence Nanodegree program
- Artificial Intelligence (AI) Online Courses | Udacity
- Intro to Artificial Intelligence Course | Udacity
- Edge AI for IoT Developers Course | Udacity
- Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
- Expert Systems and Applied Artificial Intelligence
- Autonomous Systems - Microsoft AI
- Introduction to Microsoft Project Bonsai
- Machine teaching with the Microsoft Autonomous Systems platform
- Autonomous Maritime Systems Training | AMC Search
- Top Autonomous Cars Courses Online | Udemy
- Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
- Learn Autonomous Robotics with Online Courses and Lessons | edX
- Artificial Intelligence Nanodegree program
- Autonomous Systems Online Courses & Programs | Udacity
- Edge AI for IoT Developers Course | Udacity
- Autonomous Systems MOOC and Free Online Courses | MOOC List
- Robotics and Autonomous Systems Graduate Program | Standford Online
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- NVIDIA DLSS (Deep Learning Super Sampling)
- AMD FidelityFX Super Resolution (FSR) - quality solution for producing high resolution frames from lower resolution inputs. It uses a collection of cutting-edge Deep Learning algorithms with a particular emphasis on creating high-quality edges, giving large performance improvements compared to rendering at native resolution directly. FSR enables “practical performance” for costly render operations, such as hardware ray tracing for the AMD RDNA™ and AMD RDNA™ 2 architectures.
- Intel Xe Super Sampling (XeSS) - cores to run XeSS. The GPUs will have Xe Matrix eXtenstions matrix (XMX) engines for hardware-accelerated AI processing. XeSS will be able to run on devices without XMX, including integrated graphics, though, the performance of XeSS will be lower on non-Intel graphics cards because it will be powered by [DP4a instruction](https://www.intel.com/content/dam/www/public/us/en/documents/reference-guides/11th-gen-quick-reference-guide.pdf).
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Apache PredictionIO
- Cluster Manager for Apache Kafka(CMAK)
- BigDL
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
- XGBoost
- LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- Scikit-Learn
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- Keras - level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
- PyTorch
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Microsoft Cognitive Toolkit (CNTK) - source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Apache MXNet
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- Anaconda
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Scikit-Learn
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- Microsoft Project Bonsai - code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
- Microsoft AirSim - source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
- CARLA - source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
- ROS/ROS2 bridge for CARLA(package) - way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
- ROS Toolbox
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Computer Vision Toolbox™
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
- UAV Toolbox
- Navigation Toolbox™ - based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Mapping Toolbox™
- Reinforcement Learning - supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).
- Top Reinforcement Learning Courses | Coursera
- Top Reinforcement Learning Courses | Udemy
- Top Reinforcement Learning Courses | Udacity
- Reinforcement Learning Courses | Stanford Online
- Deep Learning Online Courses | NVIDIA
- Top Deep Learning Courses Online | Coursera
- Top Deep Learning Courses Online | Udemy
- Learn Deep Learning with Online Courses and Lessons | edX
- Deep Learning Online Course Nanodegree | Udacity
- Machine Learning Course by Andrew Ng | Coursera
- Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
- Data Science: Deep Learning and Neural Networks in Python | Udemy
- Understanding Machine Learning with Python | Pluralsight
- How to Think About Machine Learning Algorithms | Pluralsight
- Deep Learning Courses | Stanford Online
- Deep Learning - UW Professional & Continuing Education
- Deep Learning Online Courses | Harvard University
- Machine Learning for Everyone Courses | DataCamp
- Artificial Intelligence Expert Course: Platinum Edition | Udemy
- Top Artificial Intelligence Courses Online | Coursera
- Learn Artificial Intelligence with Online Courses and Lessons | edX
- Professional Certificate in Computer Science for Artificial Intelligence | edX
- Artificial Intelligence Nanodegree program
- Artificial Intelligence (AI) Online Courses | Udacity
- Intro to Artificial Intelligence Course | Udacity
- Edge AI for IoT Developers Course | Udacity
- Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
- Expert Systems and Applied Artificial Intelligence
- Autonomous Systems - Microsoft AI
- Introduction to Microsoft Project Bonsai
- Machine teaching with the Microsoft Autonomous Systems platform
- Autonomous Maritime Systems Training | AMC Search
- Top Autonomous Cars Courses Online | Udemy
- Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
- Learn Autonomous Robotics with Online Courses and Lessons | edX
- Artificial Intelligence Nanodegree program
- Autonomous Systems Online Courses & Programs | Udacity
- Edge AI for IoT Developers Course | Udacity
- Autonomous Systems MOOC and Free Online Courses | MOOC List
- Robotics and Autonomous Systems Graduate Program | Standford Online
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- OpenAI
- ReinforcementLearning.jl
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Amazon SageMaker
- AWS RoboMaker - managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- Keras - level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
- PyTorch
- Scikit-Learn
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Apache PredictionIO
- Cluster Manager for Apache Kafka(CMAK)
- BigDL
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
- XGBoost
- LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Microsoft Cognitive Toolkit (CNTK) - source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Apache MXNet
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- Anaconda
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Scikit-Learn
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- Microsoft Project Bonsai - code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
- Microsoft AirSim - source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
- CARLA - source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
- ROS/ROS2 bridge for CARLA(package) - way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
- ROS Toolbox
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Computer Vision Toolbox™
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
- Navigation Toolbox™ - based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
- UAV Toolbox
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Mapping Toolbox™
- Computer Vision
- OpenCV Courses
- Exploring Computer Vision in Microsoft Azure
- Top Computer Vision Courses Online | Coursera
- Top Computer Vision Courses Online | Udemy
- Learn Computer Vision with Online Courses and Lessons | edX
- Computer Vision and Image Processing Fundamentals | edX
- Introduction to Computer Vision Courses | Udacity
- Computer Vision Nanodegree program | Udacity
- Machine Vision Course |MIT Open Courseware
- Computer Vision Training Courses | NobleProg
- Visual Computing Graduate Program | Stanford Online
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Microsoft Cognitive Toolkit (CNTK) - source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
- Scikit-Learn
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
- LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Computer Vision Toolbox™
- Statistics and Machine Learning Toolbox™
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Mapping Toolbox™
- UAV Toolbox
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
- Partial Differential Equation Toolbox™
- ROS Toolbox
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- Data Acquisition Toolbox™
- Microsoft AirSim - source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
- Natural Language Processing (NLP) - based modeling of human language with statistical, machine learning, and deep learning models.
- Natural Language Processing With Python's NLTK Package
- Cognitive Services—APIs for AI Developers | Microsoft Azure
- Artificial Intelligence Services - Amazon Web Services (AWS)
- Google Cloud Natural Language API
- Top Natural Language Processing Courses Online | Udemy
- Introduction to Natural Language Processing (NLP) | Udemy
- Top Natural Language Processing Courses | Coursera
- Natural Language Processing | Coursera
- Natural Language Processing in TensorFlow | Coursera
- Learn Natural Language Processing with Online Courses and Lessons | edX
- Build a Natural Language Processing Solution with Microsoft Azure | Pluralsight
- Natural Language Processing (NLP) Training Courses | NobleProg
- Natural Language Processing with Deep Learning Course | Standford Online
- Advanced Natural Language Processing - MIT OpenCourseWare
- Certified Natural Language Processing Expert Certification | IABAC
- Natural Language Processing Course - Intel
- Natural Language Toolkit (NLTK) - to-use interfaces to over [50 corpora and lexical resources](https://nltk.org/nltk_data/) such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.
- spaCy - task learning with pretrained transformers like BERT.
- CoreNLP
- NLPnet - of-speech tagging, semantic role labeling and dependency parsing.
- Flair - of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.
- Catalyst - trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
- Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
- Microsoft Cognitive Toolkit (CNTK) - source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- Keras - level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
- PyTorch
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
- Anaconda
- PlaidML
- Scikit-Learn
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Apache PredictionIO
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- BigDL
- Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
- Bioinformatics
- European Bioinformatics Institute
- National Center for Biotechnology Information
- Online Courses in Bioinformatics |ISCB - International Society for Computational Biology
- Bioinformatics | Coursera
- Top Bioinformatics Courses | Udemy
- Biometrics Courses | Udemy
- Learn Bioinformatics with Online Courses and Lessons | edX
- Bioinformatics Graduate Certificate | Harvard Extension School
- Bioinformatics and Biostatistics | UC San Diego Extension
- Bioinformatics and Proteomics - Free Online Course Materials | MIT
- Introduction to Biometrics course - Biometrics Institute
- Bioconductor - throughput genomic data. Bioconductor uses the [R statistical programming language](https://www.r-project.org/about.html), and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an [AMI (Amazon Machine Image)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AMIs.html) and [Docker images](https://docs.docker.com/engine/reference/commandline/images/).
- Bioconda
- UniProt - quality and freely accessible set of protein sequences annotated with functional information.
- Bowtie 2 - efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.
- Biopython
- BioRuby
- BioJava
- BioPHP
- Avogadro - platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.
- Ascalaph Designer
- Anduril - thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.
- Galaxy - based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.
- PathVisio - source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.
- Orange
- Basic Local Alignment Search Tool
- OSIRIS - domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.
- NCBI BioSystems
- NVIDIA Developer CUDA
- CUDA - accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
- CUDA Toolkit Documentation
- CUDA Quick Start Guide
- CUDA on WSL
- CUDA GPU support for TensorFlow
- NVIDIA Deep Learning cuDNN Documentation
- NVIDIA GPU Cloud Documentation
- NVIDIA NGC - optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
- NVIDIA NGC Containers - accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
- CUDA Toolkit - accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
- NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
- Minkowski Engine - differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
- CUTLASS - performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
- CUB
- Tensorman
- Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
- Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
- CuPy - compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
- CatBoost
- cuDF - like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
- cuML - learn.
- ArrayFire - purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
- Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
- AresDB - powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
- Arraymancer - dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
- Kintinuous - time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
- GraphVite - speed and large-scale embedding learning in various applications.
- MATLAB
- MATLAB Documentation
- Getting Started with MATLAB
- MATLAB and Simulink Training from MATLAB Academy
- MathWorks Certification Program
- Apache Spark Basics | MATLAB & Simulink
- MATLAB Hadoop and Spark | MATLAB & Simulink
- MATLAB Online Courses from Udemy
- MATLAB Online Courses from Coursera
- MATLAB Online Courses from edX
- Building a MATLAB GUI
- MATLAB Style Guidelines 2.0
- Setting Up Git Source Control with MATLAB & Simulink
- Pull, Push and Fetch Files with Git with MATLAB & Simulink
- Create New Repository with MATLAB & Simulink
- PRMLT
- MATLAB and Simulink Services & Applications List
- MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
- MATLAB Online™
- Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
- Simulink Online™
- MATLAB Drive™
- MATLAB Parallel Server™
- MATLAB Schemer
- LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Computer Vision Toolbox™
- Statistics and Machine Learning Toolbox™
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Mapping Toolbox™
- UAV Toolbox
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
- Partial Differential Equation Toolbox™
- ROS Toolbox
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- SoC Blockset™
- Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
- ThingSpeak™ - of-concept IoT systems that require analytics.
- SEA-MAT
- Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
- hctsa - series analysis using Matlab.
- Plotly
- YALMIP
- GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
- C++ - platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
- C - purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
- Embedded C - committee) to address issues that exist between C extensions for different [embedded systems](https://en.wikipedia.org/wiki/Embedded_system). The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
- C & C++ Developer Tools from JetBrains
- Open source C++ libraries on cppreference.com
- C++ Graphics libraries
- C++ Libraries in MATLAB
- C++ Tools and Libraries Articles
- Google C++ Style Guide
- Introduction C++ Education course on Google Developers
- C++ style guide for Fuchsia
- C and C++ Coding Style Guide by OpenTitan
- Chromium C++ Style Guide
- C++ Core Guidelines
- C++ Style Guide for ROS
- Learn C++
- Learn C : An Interactive C Tutorial
- C++ Institute
- C++ Online Training Courses on LinkedIn Learning
- C++ Tutorials on W3Schools
- Learn C Programming Online Courses on edX
- Learn C++ with Online Courses on edX
- Learn C++ on Codecademy
- Coding for Everyone: C and C++ course on Coursera
- C++ For C Programmers on Coursera
- Top C Courses on Coursera
- C++ Online Courses on Udemy
- Top C Courses on Udemy
- Basics of Embedded C Programming for Beginners on Udemy
- C++ For Programmers Course on Udacity
- C++ Fundamentals Course on Pluralsight
- Introduction to C++ on MIT Free Online Course Materials
- Introduction to C++ for Programmers | Harvard
- Online C Courses | Harvard University
- AWS SDK for C++
- Azure SDK for C++
- Azure SDK for C
- C++ Client Libraries for Google Cloud Services
- Visual Studio - rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
- Visual Studio Code
- Vcpkg
- ReSharper C++
- AppCode - fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
- CLion - platform IDE for C and C++ developers developed by JetBrains.
- Code::Blocks
- CppSharp
- Conan
- High Performance Computing (HPC) SDK
- Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
- Boost - edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
- Automake
- Cmake - source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
- GDB
- GCC - C, Fortran, Ada, Go, and D, as well as libraries for these languages.
- GSL - squares fitting. There are over 1000 functions in total with an extensive test suite.
- OpenGL Extension Wrangler Library (GLEW) - platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
- Libtool
- Maven
- TAU (Tuning And Analysis Utilities) - based sampling. All C++ language features are supported including templates and namespaces.
- Clang - C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
- OpenCV - time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Libcu++
- ANTLR (ANother Tool for Language Recognition)
- Oat++ - efficient web application. It's zero-dependency and easy-portable.
- JavaCPP
- Cython
- Spdlog - only/compiled, C++ logging library.
- Infer - C, and C. Infer is written in [OCaml](https://ocaml.org/).
- Java
- The Eclipse Foundation
- Getting Started with Java
- Oracle Java certifications from Oracle University
- Google Developers Training
- Google Developers Certification
- Java Tutorial by W3Schools
- Getting Started with Java in Visual Studio Code
- Google Java Style Guide
- AOSP Java Code Style for Contributors
- Chromium Java style guide
- Get Started with OR-Tools for Java
- Getting started with Java Tool Installer task for Azure Pipelines
- Gradle User Manual
- Java SE
- JDK Development Tools
- Android Studio
- IntelliJ IDEA
- NetBeans
- Java Design Patterns
- Elasticsearch
- RxJava - based programs by using observable sequences. It extends the [observer pattern](http://en.wikipedia.org/wiki/Observer_pattern) to support sequences of data/events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.
- Guava
- okhttp
- Retrofit - safe HTTP client for Android and Java develped by Square.
- LeakCanary
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Flink - and batch-processing capabilities with elegant and fluent APIs in Java and Scala.
- Fastjson
- libGDX - platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
- Jenkins - source automation server. Built with Java, it provides over 1700 [plugins](https://plugins.jenkins.io/) to support automating virtually anything, so that humans can actually spend their time doing things machines cannot.
- DBeaver - platform database tool for developers, SQL programmers, database administrators and analysts. Supports any database which has JDBC driver (which basically means - ANY database). EE version also supports non-JDBC datasources (MongoDB, Cassandra, Redis, DynamoDB, etc).
- Redisson - Memory Data Grid. Over 50 Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Tomcat, Scheduler, JCache API, Hibernate, MyBatis, RPC, and local cache.
- GraalVM - based languages like Java, Scala, Clojure, Kotlin, and LLVM-based languages such as C and C++.
- Gradle - language software development. From mobile apps to microservices, from small startups to big enterprises, Gradle helps teams build, automate and deliver better software, faster. Write in Java, C++, Python or your language of choice.
- Apache Groovy - typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming.
- JaCoCo
- Apache JMeter
- Junit
- Mockito
- SpotBugs
- SpringBoot - powered, production-grade applications and services with absolute minimum fuss. It takes an opinionated view of the Spring platform so that new and existing users can quickly get to the bits they need.
- YourKit
- Python - level programming language. Python is used heavily in the fields of Data Science and Machine Learning.
- Python Developer’s Guide
- Azure Functions Python developer guide - us/azure/azure-functions/functions-reference).
- CheckiO
- Python Institute
- PCEP – Certified Entry-Level Python Programmer certification
- PCAP – Certified Associate in Python Programming certification
- PCPP – Certified Professional in Python Programming 1 certification
- PCPP – Certified Professional in Python Programming 2
- MTA: Introduction to Programming Using Python Certification
- Getting Started with Python in Visual Studio Code
- Google's Python Style Guide
- Google's Python Education Class
- Real Python
- The Python Open Source Computer Science Degree by Forrest Knight
- Intro to Python for Data Science
- Intro to Python by W3schools
- Codecademy's Python 3 course
- Learn Python with Online Courses and Classes from edX
- Python Courses Online from Coursera
- Python Package Index (PyPI)
- PyCharm
- Python Tools for Visual Studio(PTVS)
- Pylance
- Pyright
- Django - level Python Web framework that encourages rapid development and clean, pragmatic design.
- Flask
- Web2py - source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.
- AWS Chalice
- Tornado - blocking network I/O, which can scale to tens of thousands of open connections.
- HTTPie
- Scrapy - level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
- Sentry
- Pipenv
- Python Fire
- Bottle - framework for Python. It is distributed as a single file module and has no dependencies other than the [Python Standard Library](https://docs.python.org/library/).
- CherryPy - oriented HTTP web framework.
- Sanic
- Pyramid - world web application development and deployment more fun and more productive.
- TurboGears
- Falcon - performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
- Neural Network Intelligence(NNI)
- Dash
- Luigi - in.
- Locust
- spaCy
- NumPy
- Pillow
- IPython
- GraphLab Create - scale, high-performance machine learning models.
- Pandas
- PuLP
- Matplotlib - quality figures in a variety of hardcopy formats and interactive environments across platforms.
- Scikit-Learn
- Scala - oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.
- Scala Style Guide
- Databricks Scala Style Guide
- Data Science using Scala and Spark on Azure
- Creating a Scala Maven application for Apache Spark in HDInsight using IntelliJ
- Intro to Spark DataFrames using Scala with Azure Databricks
- Using Scala to Program AWS Glue ETL Scripts
- Using Flink Scala shell with Amazon EMR clusters
- AWS EMR and Spark 2 using Scala from Udemy
- Using the Google Cloud Storage connector with Apache Spark
- Write and run Spark Scala jobs on Cloud Dataproc for Google Cloud
- Scala Courses and Certifications from edX
- Scala Courses from Coursera
- Top Scala Courses from Udemy
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Apache PredictionIO
- Cluster Manager for Apache Kafka(CMAK)
- BigDL
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- Play Framework
- Dotty
- AWScala
- Scala.js
- Polynote
- Scala Native - of-time compiler and lightweight managed runtime designed specifically for Scala.
- Gitbucket
- Finagle - agnostic RPC system
- Gatling - Sent-Events and JMS.
- Scalatra - performance, async web framework, inspired by [Sinatra](https://www.sinatrarb.com/).
- R
- An Introduction to R
- Google's R Style Guide
- R developer's guide to Azure
- Running R at Scale on Google Compute Engine
- Running R on AWS
- RStudio Server Pro for AWS
- Learn R by Codecademy
- Learn R Programming with Online Courses and Lessons by edX
- R Language Courses by Coursera
- Learn R For Data Science by Udacity
- Visual Studio Code
- Code Server
- VSCode-R - project.org/), including features such as extended syntax highlighting, R language service based on code analysis, interacting with R terminals, viewing data, plots, workspace variables, help pages, managing packages, and working with [R Markdown](https://rmarkdown.rstudio.com/) documents.
- R Debugger
- Language Server Protocol (LSP)
- RStudio - highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
- Shiny
- Rmarkdown
- R Host
- Rplugin
- Plotly
- Metaflow - life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
- Prophet - linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.
- LightGBM
- Dash
- MLR
- ML workspace - in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.
- CatBoost
- Plumber
- Drake - focused pipeline toolkit for reproducibility and high-performance computing.
- DiagrammeR
- Knitr - purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
- Broom
- Julia - level, [high-performance](https://julialang.org/benchmarks/) dynamic language for technical computing. Julia programs compile to efficient native code for [multiple platforms](https://julialang.org/downloads/#support_tiers) via LLVM.
- JuliaHub
- Julia Observer
- Julia Manual
- JuliaLang Essentials
- Julia Style Guide
- Julia By Example
- JuliaLang Gitter
- DataFrames Tutorial using Jupyter Notebooks
- Julia Academy
- Julia Meetup groups
- Julia on Microsoft Azure
- JuliaPro
- Juno
- Debugger.jl
- Profile (Stdlib)
- Revise.jl - compile.
- JuliaGPU - level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
- IJulia.jl
- AWS.jl
- CUDA.jl - friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
- XLA.jl
- Nanosoldier.jl
- Julia for VSCode
- JuMP.jl - specific modeling language for [mathematical optimization](https://en.wikipedia.org/wiki/Mathematical_optimization) embedded in Julia.
- Optim.jl
- RCall.jl
- JavaCall.jl
- PyCall.jl
- MXNet.jl - of-art deep learning to Julia.
- Knet
- Distributions.jl
- DataFrames.jl
- Flux.jl - Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.
- IRTools.jl
- Cassette.jl - in-time (JIT) compilation cycle, enabling post hoc analysis and modification of "Cassette-unaware" Julia programs without requiring manual source annotation or refactoring of the target code.
- Creative Commons Attribution 4.0 International (CC BY 4.0) Public License
Keywords
python
16
machine-learning
9
java
8
deep-learning
8
cpp
8
cuda
8
gpu
6
nlp
6
julia
5
data-science
5
natural-language-processing
5
curl
4
cli
4
neural-network
4
pytorch
4
nvidia
4
android
3
named-entity-recognition
3
tensorflow
3
cpp11
3
cpp14
3
cxx14
3
http
3
neural-networks
3
c
3
azure
3
matlab
3
docker
3
ai
3
data-visualization
3
artificial-intelligence
3
visual-studio
2
iot
2
visualization
2
gpu-computing
2
nvidia-hpc-sdk
2
cxx20
2
cxx17
2
cxx11
2
cxx
2
machine-learning-algorithms
2
cpp20
2
kvm
2
cpp17
2
virtualization
2
algorithms
2
compiler
2
web-framework
2
numpy
2
kotlin
2