Parallel-Computing-Guide
Parallel Computing Guide
https://github.com/mikeroyal/Parallel-Computing-Guide
Last synced: 2 days ago
JSON representation
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SQL/NoSQL Learning Resources
- Tableau CRM: BI Software and Tools
- SQL
- 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
- Databases on AWS
- Best Practices and Recommendations for SQL Server Clustering in AWS EC2.
- Connecting from Google Kubernetes Engine to a Cloud SQL instance.
- MySQL Certifications
- What is NoSQL?
- 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.
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Apache Spark Learning Resources
- Apache Spark Quick Start
- Introduction to Apache Spark and Analytics | AWS
- Apache Spark 3.0: For Analytics & Machine Learning | NVIDIA
- 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
- 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
- Apache Spark Basics | MATLAB & Simulink
- Databricks Certified Associate Developer for Apache Spark 3.0 certification | Databricks
- MATLAB Hadoop and Spark | MATLAB & Simulink
- Cloudera Developer Training for Apache Spark™ and Hadoop | Cloudera
- 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.
- What is Apache Spark? | IBM
- Apache Spark Essential Training Online Class | LinkedIn Learning
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Apache Spark Tools, Libraries, and Frameworks
- 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
- MLflow
- Apache Flume
- Tracking component
- Projects component
- Models component
- Model Registry
- Apache Arrow - independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
- 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.
- 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.
- Koalas - docs/stable/reference/api/pandas.DataFrame.html) on top of [Apache Spark](https://spark.apache.org/).
- Cluster Manager for Apache Kafka(CMAK)
- BigDL
- Apache PredictionIO
- 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.
- Hadoop Distributed File System (HDFS) - yarn/hadoop-yarn-site/YARN.html).
- Logstash
- Kibana
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
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Reinforcement Learning Tools, Libraries, and Frameworks
- Apache MXNet
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- XGBoost
- OpenAI
- ReinforcementLearning.jl
- 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.
- AWS RoboMaker - managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
- LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
- 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.
- 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.
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SQL/NoSQL Tools and Databases
- Extract, transform, and load (ETL)
- MSSQL for Visual Studio Code
- SQL Server Migration Assistant
- 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.
- 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.
- 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/).
- 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/).
- 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.
- 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.
- Sequel Pro
- 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.
- 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.
- ElasticSearch - capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
- 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.
- 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.
- 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.
- 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 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
- Atlas - memory dimensional [time series database](https://en.wikipedia.org/wiki/Time_series_database).
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Network Learning Resources
- AWS Certified Security - Specialty Certification
- Cisco Security Certifications
- The Red Hat Certified Specialist in Security: Linux
- Linux Professional Institute LPIC-3 Enterprise Security Certification
- Networking courses and specializations from Coursera
- Network & Security Courses from Udemy
- Network & Security Courses from edX
- Cisco Security Certifications
- Cybersecurity Courses and Certifications by Offensive Security
- Juniper Networks Certification Program Enterprise (JNCP)
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Networking Tools & Concepts
- 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.
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Network Protocols
- OAuth 2.0 - party applications to access the user account.
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Virtualization
- 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.
- 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.
- VMware Workstation
- VirtManager
- 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.
- Cloud Hypervisor - lang.org/) and is based on the [rust-vmm](https://github.com/rust-vmm) crates.
- 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.
- VMware vSphere Hypervisor - metal hypervisor that virtualizes servers; allowing you to consolidate your applications while saving time and money managing your IT infrastructure.
- VMware Workstation
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File systems & Storage
- 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 )
- OpenZFS - source storage platform. It includes the functionality of both traditional file systems and volume manager. It has many advanced features including:
- VMware
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Telco 5G Learning Resources
- Network Functions Virtualization Infrastructure (NFVI) by Cisco
- Red Hat telco ecosystem program
- OpenStack for Telcos by Canonical
- Open source NFV platform for 5G from Ubuntu
- Understanding 5G Technology from Verizon
- Telco Acceleration with Xilinx
- VIMs on OSM Public Wiki
- Citrix Certified Professional – Virtualization(CCP-V)
- CCNP Routing and Switching
- Wireshark Certified Network Analyst (WCNA)
- Certified Information Security Manager(CISM)
- Citrix Certified Associate – Networking(CCA-N)
- HPE(Hewlett Packard Enterprise) Telco Blueprints overview
- VMware Telco Cloud Automation(TCA) Architecture Overview
- 5G Telco Cloud from VMware
- Open source NFV platform for 5G from Ubuntu
- Understanding 5G Technology from Intel
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Telco 5G Tools and Frameworks
- StarlingX - low latency use cases.
- Airship
- 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.
- OpenRAN - vendor deployments.
- Open vSwitch(OVS)
- Multi-access edge computing (MEC) - parties across multi-vendor Multi-access Edge Computing platforms.
- Cloud-Native Network Functions(CNF)
- Physical Network Function(PNF)
- Edge
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DevOps
- 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
- Chef
- 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.
- 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.
- 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.
- Cloud Foundry
- BOSH
- 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.
- 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.
- Microsoft Azure - managed data centers.
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Continuous Integration/Continuous Delivery
- Bamboo
- Drone - compose, to define and execute Pipelines inside Docker containers.
- 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.
- Travis CI
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Microservices
- 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.
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Containers
- 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.
- 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.
- Open Container Initiative
- Buildah
- Podman
- Rancher
- Containerd - level storage to network attachments and beyond. It is available for Linux and Windows.
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Cloud Native Learning Resources
- 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 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
- Developing Cloud Native Applications course on edX
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ML Frameworks, Libraries, and Tools
- Fuzzy logic - tree processing and better integration with rules-based programming.
- ResearchGate
- Support Vector Machine (SVM) - group classification problems.
- OpenClipArt
- Convolutional Neural Networks (R-CNN)
- CS231n
- Slideteam
- wikimedia
- Decision trees - structured models for classification and regression.
- CMU
- Naive Bayes - theorem.html) with strong independence assumptions between the features.
- mathisfun
- Amazon SageMaker
- 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.
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- DeepAI
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- nGraph - of-use to AI developers.
- 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.
- cuML - learn.
- Support Vector Machine (SVM) - group classification problems.
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Reinforcement Learning Learning Resources
- Top Deep Learning Courses Online | Coursera
- Top Artificial Intelligence Courses Online | Coursera
- Professional Certificate in Computer Science for Artificial Intelligence | edX
- Autonomous Systems Online Courses & Programs | Udacity
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- Artificial Intelligence (AI) Online Courses | Udacity
- Top Reinforcement Learning Courses | Coursera
- Top Reinforcement Learning Courses | Udemy
- Top Reinforcement Learning Courses | Udacity
- Reinforcement Learning Courses | Stanford Online
- Machine Learning for Everyone Courses | DataCamp
- Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
- Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
- Introduction to Microsoft Project Bonsai
- Autonomous Systems - Microsoft AI
- Deep Learning Courses | Stanford Online
- Artificial Intelligence Expert Course: Platinum Edition | Udemy
- Data Science: Deep Learning and Neural Networks in Python | Udemy
- Top Deep Learning Courses Online | Udemy
- Expert Systems and Applied Artificial Intelligence
- 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
- Autonomous Systems MOOC and Free Online Courses | MOOC List
- Robotics and Autonomous Systems Graduate Program | Standford Online
- Learn Deep Learning with Online Courses and Lessons | edX
- Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
- Understanding Machine Learning with Python | Pluralsight
- How to Think About Machine Learning Algorithms | Pluralsight
- Deep Learning - UW Professional & Continuing Education
- Deep Learning Online Courses | Harvard University
- Deep Learning Online Course Nanodegree | Udacity
- Learn Artificial Intelligence with Online Courses and Lessons | edX
- Artificial Intelligence Nanodegree program
- Intro to Artificial Intelligence Course | Udacity
- Top Reinforcement Learning Courses | Udacity
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Learning Resources for ML
- Machine Learning by Stanford University from Coursera
- 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
- 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
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Machine Learning Crash Course for Google Cloud
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NLP Tools, Libraries, and Frameworks
- PyTorch
- 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.
- 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).
- Anaconda
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- 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.
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Deep Learning Tools, Libraries, and Frameworks
- NVIDIA DLSS (Deep Learning Super Sampling)
- 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).
- 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.
- 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.
- 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.
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Computer Vision Learning Resources
- Computer Vision
- OpenCV Courses
- 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
- Computer Vision Nanodegree program | Udacity
- Computer Vision Training Courses | NobleProg
- Visual Computing Graduate Program | Stanford Online
- Machine Vision Course |MIT Open Courseware
- Computer Vision
- OpenCV Courses
- Computer Vision and Image Processing Fundamentals | edX
- Introduction to Computer Vision Courses | Udacity
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Computer Vision Tools, Libraries, and Frameworks
- 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.
- 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.
-
NLP Learning Resources
- 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
- Cognitive Services—APIs for AI Developers | Microsoft Azure
-
Bioinformatics Learning Resources
- 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 Proteomics - Free Online Course Materials | MIT
- Introduction to Biometrics course - Biometrics Institute
-
Bioinformatics Tools, Libraries, and Frameworks
- 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
- 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.
-
CUDA Learning Resources
- CUDA Toolkit Documentation
- CUDA Quick Start Guide
- CUDA on WSL
- NVIDIA Deep Learning cuDNN Documentation
- 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 Tools Libraries, and Frameworks
- 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.
- CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
- 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.
- 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.
- 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.
- 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.
- 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.
- NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
- 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
- Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
- 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.
-
MATLAB Learning Resources
- MATLAB
- MATLAB Documentation
- 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
- Getting Started with MATLAB
- MathWorks Certification Program
- PRMLT
-
MATLAB Tools, Libraries, Frameworks
- Simulink Online™
- MATLAB and Simulink Services & Applications List
- MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
- Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
- SoC Blockset™
- ThingSpeak™ - of-concept IoT systems that require analytics.
- hctsa - series analysis using Matlab.
- YALMIP
- MATLAB Drive™
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Mapping Toolbox™
- Statistics and Machine Learning 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.
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- UAV Toolbox
- 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.
- 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.
- 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.
- Partial Differential Equation Toolbox™
- Computer Vision Toolbox™
- 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.
- hctsa - series analysis using Matlab.
- ROS Toolbox
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- MATLAB Schemer
- 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.
- 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.
-
OpenCL Learning Resources
- Open Computing Language (OpenCL) - to-parallel-computing-zNrIS) of heterogeneous platforms consisting of CPUs, GPUs, and other hardware accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms.
- OpenCL | GitHub
- Khronos Technology Courses and Training
- OpenCL Tutorials - StreamHPC
- Introduction to Intel® OpenCL Tools
- OpenCL | NVIDIA Developer
- Introduction to OpenCL on FPGAs Course | Coursera
- Compiling OpenCL Kernel to FPGAs Course | Coursera
- OpenCL Tutorials - StreamHPC
- OpenCL | GitHub
- Khronos Group | GitHub
- Khronos Technology Courses and Training
- Introduction to Intel® OpenCL Tools
-
OpenCL Tools, Libraries and Frameworks
- GPUVerify
- AMD Radeon ProRender - based rendering engine that enables creative professionals to produce stunningly photorealistic images on virtually any GPU, any CPU, and any OS in over a dozen leading digital content creation and CAD applications.
- Intel® SDK For OpenCL™ Applications - intensive workloads. Customize heterogeneous compute applications and accelerate performance with kernel-based programming.
- 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/).
- GPUVerify
- OpenCL ICD Loader
- clBLAS
- clFFT
- clSPARSE
- clRNG
- CLsmith - core environment, OpenCL. Its primary feature is the generation of random OpenCL kernels, exercising many features of the language. It also brings a novel idea of applying EMI, via dead-code injection.
- Oclgrind - races and barrier divergence, collecting instruction histograms, and for interactive OpenCL kernel debugging. The simulator is built on an interpreter for LLVM IR.
- Intel® SDK For OpenCL™ Applications - intensive workloads. Customize heterogeneous compute applications and accelerate performance with kernel-based programming.
-
Vulkan Learning Resources
- Khronos Group GitHub
- Vulkan GLSL Ray Tracing Emulator Tutorial
- Getting Started with Vulkan
- Khronos Community Forums
- Vulkan® - platform graphics and compute API that provides high-efficiency, cross-platform access to modern GPUs used in a wide variety of devices from PCs and consoles to mobile phones and embedded platforms. Vulkan is currently in development by the Khronos consortium.
- Vulkan Documentation
- Vulkan Samples
-
Vulkan Tools, Libraries, and Frameworks
- SPIR-V - level language front-ends to emit programs in a standardized intermediate form to be ingested by Vulkan, OpenGL or OpenCL drivers. It eliminates the need for high-level language front-end compilers in device drivers, significantly reducing driver complexity, enables a broad range of language and framework front-ends to run on diverse hardware architectures and encourages a vibrant ecosystem of open source analysis, porting, debug and optimization tools.
- GLFW - platform library for OpenGL, OpenGL ES and Vulkan application development. It provides a simple, platform-independent API for creating windows, contexts and surfaces, reading input, handling events, etc. GLFW natively supports Windows, macOS and Linux and other Unix-like systems. On Linux both X11 and Wayland are supported.
- Radeon™ GPU Analyzer
- NVIDIA® Nsight™ Visual Studio Edition
- Vulkan® Memory Allocator (VMA)
- AMD Open Source Driver for Vulkan® - source Vulkan driver for AMD Radeon™ graphics adapters on Linux®.
- Radeon™ Memory Visualizer (RMV)
- Radeon™ GPU Profiler
- SPIRV-Reflect - V shader bytecode in Vulkan applications.
- Vulkan® Tools
- Vulkan-Hpp
- DXVK - based translation layer for Direct3D 9/10/11 which allows running 3D applications on Linux using Wine.
- MoltenVK
- PerfDoc - platform Vulkan layer which checks Vulkan applications for [best practices on Arm Mali](https://developer.arm.com/graphics/developer-guides/mali-gpu-best-practices) devices.
- VulkanSharp
- Vortice.Vulkan - level bindings for Vulkan API.
- VKD3D-Proton
- ImGui - free graphical user interface library for C++. It outputs optimized vertex buffers that you can render anytime in your 3D-pipeline enabled application. It is fast, portable, renderer agnostic and self-contained (no external dependencies).
- Ash
- gfx-rs - level, cross-platform graphics and compute abstraction library in Rust.
- Vulkan.jl
-
C/C++ Tools and Frameworks
- Automake
- AWS SDK for C++
- 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.
- 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
- Conan
- High Performance Computing (HPC) SDK
- 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.
- 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
- 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.
- ANTLR (ANother Tool for Language Recognition)
- Oat++ - efficient web application. It's zero-dependency and easy-portable.
- Cython
- Infer - C, and C. Infer is written in [OCaml](https://ocaml.org/).
- Maven
- Azure SDK for C++
- Azure SDK for C
- C++ Client Libraries for Google Cloud Services
- Vcpkg
- CppSharp
- JavaCPP
- Spdlog - only/compiled, C++ logging library.
-
C/C++ Learning Resources
- 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
- Google C++ Style Guide
- Introduction C++ Education course on Google Developers
- C++ style guide for Fuchsia
- Chromium C++ Style Guide
- C++ Core Guidelines
- C++ Style Guide for ROS
- Learn C++
- Learn C : An Interactive C Tutorial
- 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
- 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
- C++ - platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
- C++ Tools and Libraries Articles
-
Java Learning Resources
-
Java Tools, Libraries, and Frameworks
- Java SE
- JDK Development Tools
- IntelliJ IDEA
- 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
- Retrofit - safe HTTP client for Android and Java develped by Square.
- 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.
- 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++.
- 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
- Junit
- Mockito
- SpotBugs
- YourKit
- 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).
- 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.
- Java Design Patterns
- Elasticsearch
- okhttp
- LeakCanary
- 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.
-
Python Learning Resources
- CheckiO
- Getting Started with Python in Visual Studio Code
- Google's Python Style Guide
- Google's Python Education Class
- 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
- Real Python
- PCPP – Certified Professional in Python Programming 2
- The Python Open Source Computer Science Degree by Forrest Knight
-
Python Frameworks and Tools
- Python Package Index (PyPI)
- PyCharm
- Django - level Python Web framework that encourages rapid development and clean, pragmatic design.
- 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.
- Falcon - performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
- Pillow
- IPython
- Pandas
- Matplotlib - quality figures in a variety of hardcopy formats and interactive environments across platforms.
- Python Tools for Visual Studio(PTVS)
- Scikit-Learn
- Python Tools for Visual Studio(PTVS)
- Pylance
- Pyright
- AWS Chalice
- 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/).
- Sanic
- Neural Network Intelligence(NNI)
- Luigi - in.
- Locust
- spaCy
- PuLP
-
Scala Learning Resources
- 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
- Creating a Scala Maven application for Apache Spark in HDInsight using IntelliJ
- 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
- Scala Courses from Coursera
- 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
- Top Scala Courses from Udemy
- Databricks Scala Style Guide
- Data Science using Scala and Spark on Azure
-
R Tools, Libraries, and Frameworks
- DiagrammeR
- 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.
- Rmarkdown
- 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.
- LightGBM
- MLR
- Plumber
- Drake - focused pipeline toolkit for reproducibility and high-performance computing.
- 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
- R Debugger
- CatBoost
- Visual Studio Code
- Language Server Protocol (LSP)
- R Host
- Rplugin
- 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.
-
Scala Tools and Libraries
- Scala Native - of-time compiler and lightweight managed runtime designed specifically for Scala.
- Gitbucket
- Gatling - Sent-Events and JMS.
- 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.
- Scala.js
- Polynote
- Scalatra - performance, async web framework, inspired by [Sinatra](https://www.sinatrarb.com/).
- 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
-
R Learning Resources
-
Julia Learning Resources
-
Julia Tools, Libraries and Frameworks
- JuliaPro
- Juno
- Profile (Stdlib)
- JuliaGPU - level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
- CUDA.jl - friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
- Julia for VSCode
- JuMP.jl - specific modeling language for [mathematical optimization](https://en.wikipedia.org/wiki/Mathematical_optimization) embedded in Julia.
- Knet
- DataFrames.jl
- Flux.jl - Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.
- Debugger.jl
- Revise.jl - compile.
- IJulia.jl
- AWS.jl
- Nanosoldier.jl
- Optim.jl
- RCall.jl
- PyCall.jl
- MXNet.jl - of-art deep learning to Julia.
- Distributions.jl
- IRTools.jl
-
Deep Learning Learning Resources
Categories
Reinforcement Learning Learning Resources
37
ML Frameworks, Libraries, and Tools
34
C/C++ Tools and Frameworks
31
SQL/NoSQL Tools and Databases
31
MATLAB Tools, Libraries, Frameworks
30
C/C++ Learning Resources
28
Apache Spark Tools, Libraries, and Frameworks
27
Java Tools, Libraries, and Frameworks
25
Python Frameworks and Tools
24
Julia Tools, Libraries and Frameworks
21
Vulkan Tools, Libraries, and Frameworks
21
R Tools, Libraries, and Frameworks
19
Bioinformatics Tools, Libraries, and Frameworks
19
SQL/NoSQL Learning Resources
17
NLP Learning Resources
17
Apache Spark Learning Resources
17
Telco 5G Learning Resources
17
Virtualization
16
Computer Vision Learning Resources
14
MATLAB Learning Resources
14
Learning Resources for ML
14
OpenCL Tools, Libraries and Frameworks
13
CUDA Tools Libraries, and Frameworks
13
Scala Learning Resources
13
OpenCL Learning Resources
13
Python Learning Resources
12
DevOps
12
Bioinformatics Learning Resources
11
File systems & Storage
11
Cloud Native Learning Resources
11
Reinforcement Learning Tools, Libraries, and Frameworks
11
Scala Tools and Libraries
11
Telco 5G Tools and Frameworks
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