Skip to content
/ Project-Genesis Public

The first project of the PyMachine Machine Learning Collective titled Project Genesis.

License

GPL-3.0 license
1 star 2 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

PyMachine-Collective/Project-Genesis

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
LICENSE
LICENSE
 
 
README.md
README.md
 
 

Repository files navigation

Project-Genesis

The first project of the PyMachine Machine Learning Collective titled Project Genesis.

What is machine learning

if you look it up on the internet it’ll say that it’s algorithms that can learn from observational data and can make predictions based on it. Basically: we take a set of observational data, we fit a line to it, and then we can use that line to make predictions.It is actually a division of A.I.

Types of Machine learning

Resources

Special thanks to https://github.com/josephmisiti/awesome-machine-learning/

Computer Vision

  • Scikit-Image - A collection of algorithms for image processing in Python.
  • Scikit-Opt - Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python)
  • SimpleCV - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.
  • Vigranumpy - Python bindings for the VIGRA C++ computer vision library.
  • OpenFace - Free and open source face recognition with deep neural networks.
  • PCV - Open source Python module for computer vision. [Deprecated]
  • face_recognition - Face recognition library that recognizes and manipulates faces from Python or from the command line.
  • dockerface - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container.
  • Detectron - FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. [Deprecated]
  • detectron2 - FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework.
  • albumentations - А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
  • pytessarct - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images. Python-tesseract is a wrapper for Google's Tesseract-OCR Engine.
  • imutils - A library containing Convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.
  • PyTorchCV - A PyTorch-Based Framework for Deep Learning in Computer Vision.
  • Self-supervised learning
  • neural-style-pt - A PyTorch implementation of Justin Johnson's neural-style (neural style transfer).
  • Detecto - Train and run a computer vision model with 5-10 lines of code.
  • neural-dream - A PyTorch implementation of DeepDream.
  • Openpose - A real-time multi-person keypoint detection library for body, face, hands, and foot estimation
  • Deep High-Resolution-Net - A PyTorch implementation of CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"
  • dream-creator - A PyTorch implementation of DeepDream. Allows individuals to quickly and easily train their own custom GoogleNet models with custom datasets for DeepDream.
  • Lucent - Tensorflow and OpenAI Clarity's Lucid adapted for PyTorch.
  • lightly - Lightly is a computer vision framework for self-supervised learning.
  • Learnergy - Energy-based machine learning models built upon PyTorch.
  • OpenVisionAPI - Open source computer vision API based on open source models.
  • IoT Owl - Light face detection and recognition system with huge possibilities, based on Microsoft Face API and TensorFlow made for small IoT devices like raspberry pi.

Natural Language Processing

  • pkuseg-python - A better version of Jieba, developed by Peking University.
  • NLTK - A leading platform for building Python programs to work with human language data.
  • Pattern - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
  • Quepy - A python framework to transform natural language questions to queries in a database query language.
  • TextBlob - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
  • YAlign - A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora. [Deprecated]
  • jieba - Chinese Words Segmentation Utilities.
  • SnowNLP - A library for processing Chinese text.
  • spammy - A library for email Spam filtering built on top of nltk
  • loso - Another Chinese segmentation library. [Deprecated]
  • genius - A Chinese segment based on Conditional Random Field.
  • KoNLPy - A Python package for Korean natural language processing.
  • nut - Natural language Understanding Toolkit. [Deprecated]
  • Rosetta - Text processing tools and wrappers (e.g. Vowpal Wabbit)
  • BLLIP Parser - Python bindings for the BLLIP Natural Language Parser (also known as the Charniak-Johnson parser). [Deprecated]
  • PyNLPl - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for FoLiA, but also ARPA language models, Moses phrasetables, GIZA++ alignments.
  • PySS3 - Python package that implements a novel white-box machine learning model for text classification, called SS3. Since SS3 has the ability to visually explain its rationale, this package also comes with easy-to-use interactive visualizations tools (online demos).
  • python-ucto - Python binding to ucto (a unicode-aware rule-based tokenizer for various languages).
  • python-frog - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
  • python-zpar - Python bindings for ZPar, a statistical part-of-speech-tagger, constituency parser, and dependency parser for English.
  • colibri-core - Python binding to C++ library for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
  • spaCy - Industrial strength NLP with Python and Cython.
  • PyStanfordDependencies - Python interface for converting Penn Treebank trees to Stanford Dependencies.
  • Distance - Levenshtein and Hamming distance computation. [Deprecated]
  • Fuzzy Wuzzy - Fuzzy String Matching in Python.
  • jellyfish - a python library for doing approximate and phonetic matching of strings.
  • editdistance - fast implementation of edit distance.
  • textacy - higher-level NLP built on Spacy.
  • stanford-corenlp-python - Python wrapper for Stanford CoreNLP [Deprecated]
  • CLTK - The Classical Language Toolkit.
  • Rasa - A "machine learning framework to automate text-and voice-based conversations."
  • yase - Transcode sentence (or other sequence) to list of word vector .
  • Polyglot - Multilingual text (NLP) processing toolkit.
  • DrQA - Reading Wikipedia to answer open-domain questions.
  • Dedupe - A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
  • Snips NLU - Natural Language Understanding library for intent classification and entity extraction
  • NeuroNER - Named-entity recognition using neural networks providing state-of-the-art-results
  • DeepPavlov - conversational AI library with many pre-trained Russian NLP models.
  • BigARTM - topic modelling platform.
  • NALP - A Natural Adversarial Language Processing framework built over Tensorflow.
  • DL Translate - A deep learning-based translation library between 50 languages, built with transformers.

General-Purpose Machine Learning

  • Microsoft ML for Apache Spark -> A distributed machine learning framework Apache Spark
  • Shapley -> A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
  • igel -> A delightful machine learning tool that allows you to train/fit, test and use models without writing code
  • ML Model building -> A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.
  • ML/DL project template
  • PyTorch Geometric Temporal -> A temporal extension of PyTorch Geometric for dynamic graph representation learning.
  • Little Ball of Fur -> A graph sampling extension library for NetworkX with a Scikit-Learn like API.
  • Karate Club -> An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
  • Auto_ViML -> Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal", is a comprehensive and scalable Python AutoML toolkit with imbalanced handling, ensembling, stacking and built-in feature selection. Featured in Medium article.
  • PyOD -> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.
  • steppy -> Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces a very simple interface that enables clean machine learning pipeline design.
  • steppy-toolkit -> Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
  • CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found here.
  • Couler - Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
  • 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 - automated build consisting of a web-interface, and set of 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 - Python bindings for eXtreme Gradient Boosting (Tree) Library.
  • Apache SINGA - An Apache Incubating project for developing an open source machine learning library.
  • Bayesian Methods for Hackers - Book/iPython notebooks on Probabilistic Programming in Python.
  • Featureforge A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
  • MLlib in Apache Spark - Distributed machine learning library in Spark
  • Hydrosphere Mist - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
  • scikit-learn - A Python module for machine learning built on top of SciPy.
  • metric-learn - A Python module for metric learning.
  • Intel(R) Extension for Scikit-learn - A seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.
  • SimpleAI Python implementation of many of the artificial intelligence algorithms described in the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.
  • astroML - Machine Learning and Data Mining for Astronomy.
  • graphlab-create - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
  • BigML - A library that contacts external servers.
  • pattern - Web mining module for Python.
  • NuPIC - Numenta Platform for Intelligent Computing.
  • Pylearn2 - A Machine Learning library based on Theano. [Deprecated]
  • keras - High-level neural networks frontend for TensorFlow, CNTK and Theano.
  • Lasagne - Lightweight library to build and train neural networks in Theano.
  • hebel - GPU-Accelerated Deep Learning Library in Python. [Deprecated]
  • Chainer - Flexible neural network framework.
  • prophet - Fast and automated time series forecasting framework by Facebook.
  • gensim - Topic Modelling for Humans.
  • topik - Topic modelling toolkit. [Deprecated]
  • PyBrain - Another Python Machine Learning Library.
  • Brainstorm - Fast, flexible and fun neural networks. This is the successor of PyBrain.
  • Surprise - A scikit for building and analyzing recommender systems.
  • implicit - Fast Python Collaborative Filtering for Implicit Datasets.
  • LightFM - A Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.
  • Crab - A flexible, fast recommender engine. [Deprecated]
  • python-recsys - A Python library for implementing a Recommender System.
  • thinking bayes - Book on Bayesian Analysis.
  • Image-to-Image Translation with Conditional Adversarial Networks - Implementation of image to image (pix2pix) translation from the paper by isola et al.[DEEP LEARNING]
  • Restricted Boltzmann Machines -Restricted Boltzmann Machines in Python. [DEEP LEARNING]
  • Bolt - Bolt Online Learning Toolbox. [Deprecated]
  • CoverTree - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree [Deprecated]
  • nilearn - Machine learning for NeuroImaging in Python.
  • neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful.
  • imbalanced-learn - Python module to perform under sampling and oversampling with various techniques.
  • Shogun - The Shogun Machine Learning Toolbox.
  • Pyevolve - Genetic algorithm framework. [Deprecated]
  • Caffe - A deep learning framework developed with cleanliness, readability, and speed in mind.
  • breze - Theano based library for deep and recurrent neural networks.
  • Cortex - Open source platform for deploying machine learning models in production.
  • pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
  • SKLL - A wrapper around scikit-learn that makes it simpler to conduct experiments.
  • neurolab
  • Spearmint - Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012. [Deprecated]
  • Pebl - Python Environment for Bayesian Learning. [Deprecated]
  • Theano - Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python.
  • TensorFlow - Open source software library for numerical computation using data flow graphs.
  • pomegranate - Hidden Markov Models for Python, implemented in Cython for speed and efficiency.
  • python-timbl - A Python extension module wrapping the full TiMBL C++ programming interface. Timbl is an elaborate k-Nearest Neighbours machine learning toolkit.
  • deap - Evolutionary algorithm framework.
  • pydeep - Deep Learning In Python. [Deprecated]
  • mlxtend - A library consisting of useful tools for data science and machine learning tasks.
  • neon - Nervana's high-performance Python-based Deep Learning framework [DEEP LEARNING]. [Deprecated]
  • Optunity - A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search.
  • Neural Networks and Deep Learning - Code samples for my book "Neural Networks and Deep Learning" [DEEP LEARNING].
  • Annoy - Approximate nearest neighbours implementation.
  • TPOT - Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine learning.
  • pgmpy A python library for working with Probabilistic Graphical Models.
  • DIGITS - The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.
  • Orange - Open source data visualization and data analysis for novices and experts.
  • MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
  • milk - Machine learning toolkit focused on supervised classification. [Deprecated]
  • TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
  • REP - an IPython-based environment for conducting data-driven research in a consistent and reproducible way. REP is not trying to substitute scikit-learn, but extends it and provides better user experience. [Deprecated]
  • rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library.
  • skbayes - Python package for Bayesian Machine Learning with scikit-learn API.
  • fuku-ml - Simple machine learning library, including Perceptron, Regression, Support Vector Machine, Decision Tree and more, it's easy to use and easy to learn for beginners.
  • Xcessiv - A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
  • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
  • PyTorch Lightning - The lightweight PyTorch wrapper for high-performance AI research.
  • PyTorch Lightning Bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.
  • skorch - A scikit-learn compatible neural network library that wraps PyTorch.
  • ML-From-Scratch - Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
  • Edward - A library for probabilistic modeling, inference, and criticism. Built on top of TensorFlow.
  • xRBM - A library for Restricted Boltzmann Machine (RBM) and its conditional variants in Tensorflow.
  • 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 - Implementation of machine learning stacking technique as a handy library in Python.
  • modAL - A modular active learning framework for Python, built on top of scikit-learn.
  • Cogitare: A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
  • Parris - Parris, the automated infrastructure setup tool for machine learning algorithms.
  • 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 - 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.
  • 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.
  • mlens - A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
  • Netron - Visualizer for machine learning models.
  • Thampi - Machine Learning Prediction System on AWS Lambda
  • MindsDB - Open Source framework to streamline use of neural networks.
  • Microsoft Recommenders: Examples and best practices for building recommendation systems, provided as Jupyter notebooks. The repo contains some of the latest state of the art algorithms from Microsoft Research as well as from other companies and institutions.
  • StellarGraph: Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.
  • BentoML: Toolkit for package and deploy machine learning models for serving in production
  • MiraiML: An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.
  • numpy-ML: Reference implementations of ML models written in numpy
  • Neuraxle: A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
  • Cornac - A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.
  • JAX - JAX is Autograd and XLA, brought together for high-performance machine learning research.
  • Catalyst - High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, rather than write another regular train loop.
  • Fastai - High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.
  • scikit-multiflow - A machine learning framework for multi-output/multi-label and stream data.
  • Lightwood - A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code.
  • bayeso - A simple, but essential Bayesian optimization package, written in Python.
  • mljar-supervised - An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides explanations and markdown reports.
  • evostra - A fast Evolution Strategy implementation in Python.
  • Determined - Scalable deep learning training platform, including integrated support for distributed training, hyperparameter tuning, experiment tracking, and model management.
  • PySyft - A Python library for secure and private Deep Learning built on PyTorch and TensorFlow.
  • PyGrid - Peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft
  • sktime - A unified framework for machine learning with time series
  • OPFython - A Python-inspired implementation of the Optimum-Path Forest classifier.
  • Opytimizer - Python-based meta-heuristic optimization techniques.
  • Gradio - A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without deploying anything.
  • Hub - Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit activeloop.ai for more info.
  • Synthia - Multidimensional synthetic data generation in Python.
  • ByteHub - An easy-to-use, Python-based feature store. Optimized for time-series data.
  • Backprop - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
  • River: A framework for general purpose online machine learning.
  • FEDOT: An AutoML framework for the automated design of composite modeling pipelines. It can handle classification, regression, and time series forecasting tasks on different types of data (including multi-modal datasets).
  • Sklearn-genetic-opt: An AutoML package for hyperparameters tuning using evolutionary algorithms, with built-in callbacks, plotting, remote logging and more.
  • Evidently: Interactive reports to analyze machine learning models during validation or production monitoring.

Data Analysis / Data Visualization

  • DataVisualization - A Github Repository Where you can Learn Datavisualizatoin Basics to Intermediate level.
  • Cartopy - Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses.
  • SciPy - A Python-based ecosystem of open-source software for mathematics, science, and engineering.
  • NumPy - A fundamental package for scientific computing with Python.
  • AutoViz AutoViz performs automatic visualization of any dataset with a single line of Python code. Give it any input file (CSV, txt or json) of any size and AutoViz will visualize it. See Medium article.
  • Numba - Python JIT (just in time) compiler to LLVM aimed at scientific Python by the developers of Cython and NumPy.
  • Mars - A tensor-based framework for large-scale data computation which is often regarded as a parallel and distributed version of NumPy.
  • NetworkX - A high-productivity software for complex networks.
  • igraph - binding to igraph library - General purpose graph library.
  • Pandas - A library providing high-performance, easy-to-use data structures and data analysis tools.
  • ParaMonte - A general-purpose Python library for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found here.
  • Open Mining - Business Intelligence (BI) in Python (Pandas web interface) [Deprecated]
  • PyMC - Markov Chain Monte Carlo sampling toolkit.
  • zipline - A Pythonic algorithmic trading library.
  • PyDy - Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
  • SymPy - A Python library for symbolic mathematics.
  • statsmodels - Statistical modeling and econometrics in Python.
  • astropy - A community Python library for Astronomy.
  • matplotlib - A Python 2D plotting library.
  • bokeh - Interactive Web Plotting for Python.
  • plotly - Collaborative web plotting for Python and matplotlib.
  • altair - A Python to Vega translator.
  • d3py - A plotting library for Python, based on D3.js.
  • PyDexter - Simple plotting for Python. Wrapper for D3xterjs; easily render charts in-browser.
  • ggplot - Same API as ggplot2 for R. [Deprecated]
  • ggfortify - Unified interface to ggplot2 popular R packages.
  • Kartograph.py - Rendering beautiful SVG maps in Python.
  • pygal - A Python SVG Charts Creator.
  • PyQtGraph - A pure-python graphics and GUI library built on PyQt4 / PySide and NumPy.
  • pycascading [Deprecated]
  • Petrel - Tools for writing, submitting, debugging, and monitoring Storm topologies in pure Python.
  • Blaze - NumPy and Pandas interface to Big Data.
  • emcee - The Python ensemble sampling toolkit for affine-invariant MCMC.
  • windML - A Python Framework for Wind Energy Analysis and Prediction.
  • vispy - GPU-based high-performance interactive OpenGL 2D/3D data visualization library.
  • cerebro2 A web-based visualization and debugging platform for NuPIC. [Deprecated]
  • NuPIC Studio An all-in-one NuPIC Hierarchical Temporal Memory visualization and debugging super-tool! [Deprecated]
  • SparklingPandas Pandas on PySpark (POPS).
  • Seaborn - A python visualization library based on matplotlib.
  • bqplot - An API for plotting in Jupyter (IPython).
  • pastalog - Simple, realtime visualization of neural network training performance.
  • Superset - A data exploration platform designed to be visual, intuitive, and interactive.
  • Dora - Tools for exploratory data analysis in Python.
  • Ruffus - Computation Pipeline library for python.
  • SOMPY - Self Organizing Map written in Python (Uses neural networks for data analysis).
  • somoclu Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters, has python API.
  • HDBScan - implementation of the hdbscan algorithm in Python - used for clustering
  • visualize_ML - A python package for data exploration and data analysis. [Deprecated]
  • scikit-plot - A visualization library for quick and easy generation of common plots in data analysis and machine learning.
  • Bowtie - A dashboard library for interactive visualizations using flask socketio and react.
  • lime - Lime is about explaining what machine learning classifiers (or models) are doing. It is able to explain any black box classifier, with two or more classes.
  • PyCM - PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters
  • Dash - A framework for creating analytical web applications built on top of Plotly.js, React, and Flask
  • Lambdo - A workflow engine for solving machine learning problems by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation via user-defined (Python) functions.
  • TensorWatch - Debugging and visualization tool for machine learning and data science. It extensively leverages Jupyter Notebook to show real-time visualizations of data in running processes such as machine learning training.
  • dowel - A little logger for machine learning research. Output any object to the terminal, CSV, TensorBoard, text logs on disk, and more with just one call to logger.log().

Misc Scripts / iPython Notebooks / Codebases

Neural Networks

  • nn_builder - nn_builder is a python package that lets you build neural networks in 1 line

  • NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

  • Neuron - Neuron is simple class for time series predictions. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm.

  • NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. [Deprecated]

  • Neuron - Neuron is simple class for time series predictions. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. [Deprecated]

  • Data Driven Code - Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.

  • Machine Learning, Data Science and Deep Learning with Python - LiveVideo course that covers machine learning, Tensorflow, artificial intelligence, and neural networks.

  • TResNet: High Performance GPU-Dedicated Architecture - TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs.

  • TResNet: Simple and powerful neural network library for python - Variety of supported types of Artificial Neural Network and learning algorithms.

  • Jina AI An easier way to build neural search in the cloud. Compatible with Jupyter Notebooks.

  • sequitur PyTorch library for creating and training sequence autoencoders in just two lines of code

Kaggle Competition Source Code

Reinforcement Learning

  • 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 - OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.
  • Serpent.AI - 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 - 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 - Open-source software for robot simulation, integrated with OpenAI Gym.
  • Retro - Retro Games in Gym
  • SLM Lab - Modular Deep Reinforcement Learning framework in PyTorch.
  • Coach - Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
  • garage - A toolkit for reproducible reinforcement learning research
  • metaworld - An open source robotics benchmark for meta- and multi-task reinforcement learning
  • acme - An Open Source Distributed Framework for Reinforcement Learning that makes build and train your agents easily.
  • Spinning Up - An educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning
  • Maze - Application-oriented deep reinforcement learning framework addressing real-world decision problems.

About

The first project of the PyMachine Machine Learning Collective titled Project Genesis.

Resources

Readme

License

GPL-3.0 license
Activity
Custom properties

Stars

1 star

Watchers

0 watching

Forks

2 forks
Report repository

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •