TensorFlow-Guide
TensorFlow Guide
https://github.com/mikeroyal/TensorFlow-Guide
Last synced: 5 days ago
JSON representation
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JavaScript Tools, Libraries, and Frameworks
- Neuro.js
- Stdlib
- Deeplearn.js - accelerated JavaScript library for machine intelligence. It brings performant machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode.
- WebStorm - the-fly error detection, powerful navigation and refactoring for JavaScript, TypeScript, stylesheet languages, and all the most popular frameworks([Angular](https://angular.io/), [React](https://reactjs.org/), [Vue.js](https://vuejs.org/), [Ionic](https://ionicframework.com/), [Apache Cordova](https://cordova.apache.org/), [React Native](https://reactnative.dev/), [Node.js](https://nodejs.org/), [Meteor](https://www.meteor.com/#!), and [Electron](https://www.electronjs.org/)).
- JavaScriptCore - C, and C-based apps. You can also use JavaScriptCore to insert custom objects into the JavaScript environment.
- Gatsby
- Ember.js
- Nest.js - side applications. It uses modern JavaScript, is built with TypeScript (preserves compatibility with pure JavaScript) and combines elements of OOP (Object Oriented Programming), FP (Functional Programming), and FRP (Functional Reactive Programming).
- Meteor - simple environment for building modern web applications with JavaScript.
- AngularJS
- Svelte
- Ionic Framework - end SDK for building cross-platform mobile apps. Built on top of [Angular](https://angular.io/) and [Apache Cordova](https://cordova.apache.org/), Ionic also provides a platform for integrating services like push notifications and analytics.
- Capacitor - platform JavaScript API and code execution layer that makes it easy to call Native SDKs from web code and to write custom native plugins that your app may need. Additionally, Capacitor provides first-class Progressive Web App support so you can write one app and deploy it to the app stores and the mobile web.
- jQuery - rich JavaScript library. It makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler with an easy-to-use API that works across a multitude of web browsers.
- Backbone.js - value binding and custom events, collections with a rich API of enumerable functions, views with declarative event handling, and connects it all to your existing API over a RESTful JSON interface.
- HTML (HyperText Markup Language)
- Cascading Style Sheets (CSS)
- RxDB - database for JavaScript Applications like Websites, hybrid Apps, Electron-Apps, Progressive Web Apps and NodeJs.
- Natural - language-processing).
- Ml.js
- DeepForge - source visual development environment for deep learning providing end-to-end support for creating deep learning models.
- Synaptic - free, so you can build and train basically any type of first order or even second order neural network architectures. This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network.
- WebDNN
- Mind
- React Native
- Next.js - fetching, and more.
- Electron - platform desktop applications using JavaScript, HTML and CSS. It is based on [Node.js](https://nodejs.org/) and [Chromium](https://www.chromium.org/) and is used by the [Atom editor](https://github.com/atom/atom) and many other [apps](https://electronjs.org/apps).
- axios
- TypeORM
- Enzyme
- Redux
- Expo - source platform for making universal native apps with React.
- Swift for TensorFlow
- Node.js - side scripts outside of a browser.
- Neuro.js
- Apache Cordova - platform development, avoiding each mobile platform's native development language.
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Julia Learning Resources
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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
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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
- Learning Machine learning and artificial intelligence from Google Cloud Training
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MATLAB Learning Resources
- MATLAB
- MATLAB Documentation
- Getting Started with MATLAB
- 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
- MathWorks Certification Program
- PRMLT
- MATLAB and Simulink Training from MATLAB Academy
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MATLAB Tools, Libraries, Frameworks
- 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.
- 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.
- Simulink Online™
- MATLAB Drive™
- 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.
- hctsa - series analysis using Matlab.
- YALMIP
- hctsa - series analysis using Matlab.
- 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.
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ML Frameworks, Libraries, and Tools
- 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.
- nGraph - of-use to AI developers.
- Tensorman
- cuML - learn.
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- 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.
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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
- Natural Language Processing (NLP) - based modeling of human language with statistical, machine learning, and deep learning models.
- Natural Language Processing Course - Intel
- Cognitive Services—APIs for AI Developers | Microsoft Azure
- Natural Language Processing Course - Intel
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NLP Tools, Libraries, and Frameworks
- PyTorch
- 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).
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Anaconda
- 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/).
- 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 PredictionIO
- 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.
- 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.
- 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.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- PlaidML
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- 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.
- 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.
- 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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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)
- Python Tools for Visual Studio(PTVS)
- Pylance
- Pyright
- AWS Chalice
- HTTPie
- 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/).
- Neural Network Intelligence(NNI)
- Luigi - in.
- Locust
- spaCy
- PuLP
- Sanic
- GraphLab Create - scale, high-performance machine learning models.
- Sentry
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Python Learning Resources
- CheckiO
- PCPP – Certified Professional in Python Programming 2
- 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
Programming Languages
Categories
Reinforcement Learning Learning Resources
40
Distributed Computing Tools, Libraries, and Frameworks
39
JavaScript Tools, Libraries, and Frameworks
38
C/C++ Tools and Frameworks
31
C/C++ Learning Resources
29
Python Frameworks and Tools
26
NLP Tools, Libraries, and Frameworks
22
Distributed Computing Learning Resources
21
R Tools, Libraries, and Frameworks
21
Julia Tools, Libraries and Frameworks
21
Swift Tools and Frameworks
21
Computer Vision Tools, Libraries, and Frameworks
20
NLP Learning Resources
20
Swift Learning Resources
19
MATLAB Learning Resources
17
Computer Vision Learning Resources
17
JavaScript Learning Resources
17
MATLAB Tools, Libraries, Frameworks
16
Scala Learning Resources
15
Learning Resources for ML
14
Python Learning Resources
12
Reinforcement Learning Tools, Libraries, and Frameworks
12
CUDA Tools Libraries, and Frameworks
12
R Learning Resources
11
Scala Tools and Libraries
11
Julia Learning Resources
10
ML Frameworks, Libraries, and Tools
7
CUDA Learning Resources
6
Deep Learning Tools, Libraries, and Frameworks
3
Deep Learning Learning Resources
3
License
1
Sub Categories
Keywords
python
15
machine-learning
11
deep-learning
10
cuda
8
swift
8
cpp
8
gpu
7
javascript
6
nlp
6
julia
5
neural-network
5
tensorflow
5
natural-language-processing
5
nvidia
4
ios
4
web-framework
4
pytorch
4
data-science
4
carthage
4
ai
3
artificial-intelligence
3
compiler
3
named-entity-recognition
3
c
3
xcode
3
neural-networks
3
react
3
matlab
3
react-native
3
scala
3
cpp14
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http
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framework
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cpp11
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cxx14
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package-manager
2
iot
2
azure-sdk
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azure
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database
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numpy
2
deep-neural-networks
2
java
2
data-visualization
2
algorithms
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nodejs
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performance
2
optimization
2
sqlite
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typescript
2