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Merge pull request #635 from joaogui1/more-resources 

Added Jax, swift for tensorflow and fastai libraries
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@ -233,8 +233,8 @@ Further resources:
* [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - A fast SVM library on GPUs and CPUs.
* [LKYDeepNN](https://github.com/mosdeo/LKYDeepNN) - A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.
* [xLearn](https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems.
* [Featuretools](https://github.com/featuretools/featuretools) - A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives".
* [skynet](https://github.com/Tyill/skynet) - A library for learning neural network, has C-interface, net set in JSON. Written in C++ with bindings in Python, C++ and C#.
* [Featuretools](https://github.com/featuretools/featuretools) - A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives".
* [skynet](https://github.com/Tyill/skynet) - A library for learning neural network, has C-interface, net set in JSON. Written in C++ with bindings in Python, C++ and C#.
* [Feast](https://github.com/gojek/feast) - A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
* [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform for reproducible and scalable machine learning and deep learning.
@ -397,9 +397,9 @@ Further resources:
* [RF](https://github.com/fxsjy/RF.go) - Random forests implementation in Go. **[Deprecated]**
<a name="go-computer-vision"></a>
#### Computer vision
#### Computer vision
* [GoCV](https://github.com/hybridgroup/gocv) - Package for computer vision using OpenCV 4 and beyond.
* [GoCV](https://github.com/hybridgroup/gocv) - Package for computer vision using OpenCV 4 and beyond.
<a name="haskell"></a>
## Haskell
@ -852,7 +852,7 @@ on MNIST digits[DEEP LEARNING].
### Data Analysis / Data Visualization
* [Perl Data Language](https://metacpan.org/pod/Paws::MachineLearning), a pluggable architecture for data and image processing, which can
be [used for machine learning](https://github.com/zenogantner/PDL-ML).
be [used for machine learning](https://github.com/zenogantner/PDL-ML).
<a name="perl-ml"></a>
### General-Purpose Machine Learning
@ -879,7 +879,7 @@ using AWS machine learning platform from Perl.
* [Perl Data Language](https://metacpan.org/pod/Paws::MachineLearning),
a pluggable architecture for data and image processing, which can
be
[used for machine learning](https://github.com/zenogantner/PDL-ML).
[used for machine learning](https://github.com/zenogantner/PDL-ML).
### General-Purpose Machine Learning
@ -922,7 +922,7 @@ be
<a name="python-nlp"></a>
#### Natural Language Processing
* [pkuseg-python](https://github.com/lancopku/pkuseg-python) - A better version of Jieba, developed by Peking University.
* [pkuseg-python](https://github.com/lancopku/pkuseg-python) - A better version of Jieba, developed by Peking University.
* [NLTK](https://www.nltk.org/) - A leading platform for building Python programs to work with human language data.
* [Pattern](http://www.clips.ua.ac.be/pattern) - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
* [Quepy](https://github.com/machinalis/quepy) - A python framework to transform natural language questions to queries in a database query language.
@ -967,7 +967,7 @@ be
* [steppy](https://github.com/neptune-ml/steppy) -> Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
* [steppy-toolkit](https://github.com/neptune-ml/steppy-toolkit) -> Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
* [CNTK](https://github.com/Microsoft/CNTK) - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found [here](https://docs.microsoft.com/cognitive-toolkit/).
* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
* [machine learning](https://github.com/jeff1evesque/machine-learning) - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
* [XGBoost](https://github.com/dmlc/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library.
* [Apache SINGA](https://singa.apache.org) - An Apache Incubating project for developing an open source machine learning library.
@ -1046,7 +1046,7 @@ be
* [CatBoost](https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, well documented and supports CPU and GPU (even multi-GPU) computation.
* [stacked_generalization](https://github.com/fukatani/stacked_generalization) - Implementation of machine learning stacking technic as handy library in Python.
* [modAL](https://github.com/modAL-python/modAL) - A modular active learning framework for Python, built on top of scikit-learn.
* [Cogitare](https://github.com/cogitare-ai/cogitare): A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
* [Cogitare](https://github.com/cogitare-ai/cogitare): A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
* [Parris](https://github.com/jgreenemi/Parris) - Parris, the automated infrastructure setup tool for machine learning algorithms.
* [neonrvm](https://github.com/siavashserver/neonrvm) - neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.
* [Turi Create](https://github.com/apple/turicreate) - Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
@ -1063,14 +1063,15 @@ be
* [creme](https://github.com/creme-ml/creme): A framework for online machine learning.
* [Neuraxle](https://github.com/Neuraxio/Neuraxle): A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
* [Cornac](https://github.com/PreferredAI/cornac) - A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.
* [JAX](https://github.com/google/jax) - JAX is Autograd and XLA, brought together for high-performance machine learning research.
* [fast.ai](https://github.com/fastai/fastaihttps://github.com/fastai/fastai) - A library simplifies training fast and accurate neural nets using modern best practices and already supports vision, text, tabular, and collab (collaborative filtering) models "out of the box"
<a name="python-data-analysis"></a>
#### Data Analysis / Data Visualization
* [SciPy](https://www.scipy.org/) - A Python-based ecosystem of open-source software for mathematics, science, and engineering.
* [NumPy](https://www.numpy.org/) - A fundamental package for scientific computing with Python.
* [Numba](https://numba.pydata.org/) - Python JIT (just in time) compiler to LLVM aimed at scientific Python by the developers of Cython and NumPy.
* [Mars](https://github.com/mars-project/mars) - A tensor-based framework for large-scale data computation which often regarded as a parallel and distributed version of NumPy.
* [Mars](https://github.com/mars-project/mars) - A tensor-based framework for large-scale data computation which often regarded as a parallel and distributed version of NumPy.
* [NetworkX](https://networkx.github.io/) - A high-productivity software for complex networks.
* [igraph](https://igraph.org/python/) - binding to igraph library - General purpose graph library.
* [Pandas](https://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.
@ -1121,7 +1122,7 @@ be
<a name="python-misc"></a>
#### Misc Scripts / iPython Notebooks / Codebases
* [Map/Reduce implementations of common ML algorithms](https://github.com/Yannael/BigDataAnalytics_INFOH515): Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
* [Map/Reduce implementations of common ML algorithms](https://github.com/Yannael/BigDataAnalytics_INFOH515): Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
* [BioPy](https://github.com/jaredthecoder/BioPy) - Biologically-Inspired and Machine Learning Algorithms in Python. **[Deprecated]**
* [SVM Explorer](https://github.com/plotly/dash-svm) - Interactive SVM Explorer, using Dash and scikit-learn
* [pattern_classification](https://github.com/rasbt/pattern_classification)
@ -1208,7 +1209,7 @@ be
#### Reinforcement Learning
* [DeepMind Lab](https://github.com/deepmind/lab) - DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning.
* [Gym](https://github.com/openai/gym) - OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.
* [Serpent.AI](https://github.com/SerpentAI/SerpentAI) - Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.
* [Serpent.AI](https://github.com/SerpentAI/SerpentAI) - Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.
* [ViZDoom](https://github.com/mwydmuch/ViZDoom) - ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.
* [Roboschool](https://github.com/openai/roboschool) - Open-source software for robot simulation, integrated with OpenAI Gym.
* [Retro](https://github.com/openai/retro) - Retro Games in Gym
@ -1468,6 +1469,7 @@ be
* [Bender](https://github.com/xmartlabs/Bender) - Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
* [Swift AI](https://github.com/Swift-AI/Swift-AI) - Highly optimized artificial intelligence and machine learning library written in Swift.
* [Swift for Tensorflow](https://github.com/tensorflow/swift) - a next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond.
* [BrainCore](https://github.com/alejandro-isaza/BrainCore) - The iOS and OS X neural network framework.
* [swix](https://github.com/stsievert/swix) - A bare bones library that includes a general matrix language and wraps some OpenCV for iOS development. **[Deprecated]**
* [AIToolbox](https://github.com/KevinCoble/AIToolbox) - A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians.