Awesome List Updates on Aug 31, 2019

4 awesome lists updated today.

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1. Awesome Machine Learning

Go / General-Purpose Machine Learning

• go-dnn (⭐6) - Deep Neural Networks for Golang (powered by MXNet)

2. Awesome Aws

Open Source Repos / Glacier

• vsespb/mt-aws-glacier 🔥🔥🔥 (⭐535) - Perl Multithreaded Multipart sync to Glacier.

3. Awesome Ruby

Database Tools

• Rein (⭐670) - Database constraints made easy for ActiveRecord.

4. Awesome Python Data Science

Machine Learning / General Purpose Machine Learning

• scikit-learn - Machine learning in Python.

• cuML (⭐4.4k) - RAPIDS Machine Learning Library.

• modAL (⭐2.3k) - Modular active learning framework for Python3.

• Sparkit-learn (⭐1.2k) - PySpark + scikit-learn = Sparkit-learn.

• MLxtend (⭐5k) - Extension and helper modules for Python's data analysis and machine learning libraries.

• Reproducible Experiment Platform (REP) (⭐691) - Machine Learning toolbox for Humans.

• scikit-multilearn (⭐929) - Multi-label classification for python.

• seqlearn (⭐693) - Sequence classification toolkit for Python.

• pystruct (⭐665) - Simple structured learning framework for Python.

• RuleFit (⭐413) - Implementation of the rulefit.

• metric-learn (⭐1.4k) - Metric learning algorithms in Python.

Machine Learning / Gradient Boosting

• XGBoost (⭐27k) - Scalable, Portable, and Distributed Gradient Boosting.

• LightGBM (⭐17k) - A fast, distributed, high-performance gradient boosting.

• CatBoost (⭐8.3k) - An open-source gradient boosting on decision trees library.

• ThunderGBM (⭐693) - Fast GBDTs and Random Forests on GPUs.

Machine Learning / Ensemble Methods

• ML-Ensemble - High performance ensemble learning.

• Stacking (⭐223) - Simple and useful stacking library written in Python.

• stacked_generalization (⭐117) - Library for machine learning stacking generalization.

• vecstack (⭐691) - Python package for stacking (machine learning technique).

Machine Learning / Imbalanced Datasets

• imbalanced-learn (⭐6.9k) - Module to perform under-sampling and over-sampling with various techniques.

• imbalanced-algorithms (⭐236) - Python-based implementations of algorithms for learning on imbalanced data.

Machine Learning / Random Forests

• rpforest (⭐223) - A forest of random projection trees.

• sklearn-random-bits-forest (⭐9) - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).

• rgf_python (⭐380) - Python Wrapper of Regularized Greedy Forest.

Machine Learning / Kernel Methods

• pyFM (⭐924) - Factorization machines in python.

• fastFM (⭐1.1k) - A library for Factorization Machines.

• tffm (⭐782) - TensorFlow implementation of an arbitrary order Factorization Machine.

• scikit-rvm (⭐234) - Relevance Vector Machine implementation using the scikit-learn API.

• ThunderSVM (⭐1.6k) - A fast SVM Library on GPUs and CPUs.

Deep Learning / PyTorch

• PyTorch (⭐87k) - Tensors and Dynamic neural networks in Python with strong GPU acceleration.

• ignite (⭐4.6k) - High-level library to help with training neural networks in PyTorch.

• skorch (⭐6k) - A scikit-learn compatible neural network library that wraps PyTorch.

Deep Learning / TensorFlow

• TensorFlow (⭐188k) - Computation using data flow graphs for scalable machine learning by Google.

• TensorLayer (⭐7.3k) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer.

• TFLearn (⭐9.6k) - Deep learning library featuring a higher-level API for TensorFlow.

• Sonnet (⭐9.8k) - TensorFlow-based neural network library.

• Polyaxon (⭐3.6k) - A platform that helps you build, manage and monitor deep learning models.

• tfdeploy (⭐354) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy.

• tensorflow-upstream (⭐690) - TensorFlow ROCm port.

• TensorFlow Fold (⭐1.8k) - Deep learning with dynamic computation graphs in TensorFlow.

• TensorLight (⭐11) - A high-level framework for TensorFlow.

• Mesh TensorFlow (⭐1.6k) - Model Parallelism Made Easier.

• Ludwig (⭐11k) - A toolbox that allows one to train and test deep learning models without the need to write code.

Automated Machine Learning / Others

• auto-sklearn (⭐7.7k) - An AutoML toolkit and a drop-in replacement for a scikit-learn estimator.

• TPOT (⭐9.8k) - AutoML tool that optimizes machine learning pipelines using genetic programming.

Natural Language Processing / Others

• torchtext (⭐3.5k) - Data loaders and abstractions for text and NLP.

• pyMorfologik (⭐18) - Python binding for Morfologik.

• skift (⭐233) - Scikit-learn wrappers for Python fastText.

• flair (⭐14k) - Very simple framework for state-of-the-art NLP.

Computer Audition / Others

• torchaudio (⭐2.6k) - An audio library for PyTorch.

Computer Vision / Others

• torchvision (⭐17k) - Datasets, Transforms, and Models specific to Computer Vision.

Graph Machine Learning / Others

• pytorch_geometric (⭐22k) - Geometric Deep Learning Extension Library for PyTorch.

Probabilistic Methods / Others

• pyro (⭐8.7k) - A flexible, scalable deep probabilistic programming library built on PyTorch.

• ZhuSuan - Bayesian Deep Learning.

• GPflow - Gaussian processes in TensorFlow.

• InferPy (⭐148) - Deep Probabilistic Modelling Made Easy.

• sklearn-bayes (⭐515) - Python package for Bayesian Machine Learning with scikit-learn API.

• skpro (⭐257) - Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute.

• PyVarInf (⭐358) - Bayesian Deep Learning methods with Variational Inference for PyTorch.

• GPyTorch (⭐3.6k) - A highly efficient and modular implementation of Gaussian Processes in PyTorch.

• sklearn-crfsuite (⭐426) - A scikit-learn-inspired API for CRFsuite.

Model Explanation / Others

• Contrastive Explanation (⭐45) - Contrastive Explanation (Foil Trees).

• yellowbrick (⭐4.3k) - Visual analysis and diagnostic tools to facilitate machine learning model selection.

• scikit-plot (⭐2.4k) - An intuitive library to add plotting functionality to scikit-learn objects.

• shap (⭐23k) - A unified approach to explain the output of any machine learning model.

• Lime (⭐12k) - Explaining the predictions of any machine learning classifier.

• FairML (⭐362) - FairML is a python toolbox auditing the machine learning models for bias.

• model-analysis (⭐1.3k) - Model analysis tools for TensorFlow.

• themis-ml (⭐125) - A library that implements fairness-aware machine learning algorithms.

• treeinterpreter (⭐753) - Interpreting scikit-learn's decision tree and random forest predictions.

Genetic Programming / Others

• gplearn (⭐1.7k) - Genetic Programming in Python.

• karoo_gp (⭐162) - A Genetic Programming platform for Python with GPU support.

• sklearn-genetic (⭐324) - Genetic feature selection module for scikit-learn.

Optimization / Others

• BoTorch (⭐3.2k) - Bayesian optimization in PyTorch.

• hyperopt-sklearn (⭐1.6k) - Hyper-parameter optimization for sklearn.

• sklearn-deap (⭐772) - Use evolutionary algorithms instead of gridsearch in scikit-learn.

• sigopt_sklearn (⭐75) - SigOpt wrappers for scikit-learn methods.

• GPflowOpt (⭐270) - Bayesian Optimization using GPflow.

Feature Engineering / General

• skl-groups (⭐41) - A scikit-learn addon to operate on set/"group"-based features.

• Feature Forge (⭐381) - A set of tools for creating and testing machine learning features.

• few (⭐51) - A feature engineering wrapper for sklearn.

• scikit-mdr (⭐125) - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.

• tsfresh (⭐8.6k) - Automatic extraction of relevant features from time series.

Feature Engineering / Feature Selection

• scikit-feature (⭐1.5k) - Feature selection repository in Python.

• boruta_py (⭐1.6k) - Implementations of the Boruta all-relevant feature selection method.

• BoostARoota (⭐220) - A fast xgboost feature selection algorithm.

• scikit-rebate (⭐415) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.

Statistics / NLP

• pandas_summary (⭐513) - Extension to pandas dataframes describe function.

• Pandas Profiling (⭐13k) - Create HTML profiling reports from pandas DataFrame objects.

• Alphalens (⭐3.5k) - Performance analysis of predictive (alpha) stock factors.

Data Manipulation / Data Frames

• datatable (⭐1.8k) - Data.table for Python.

• cuDF (⭐8.7k) - GPU DataFrame Library.

• blaze (⭐3.2k) - NumPy and pandas interface to Big Data.

• pandasql (⭐1.3k) - Allows you to query pandas DataFrames using SQL syntax.

• pandas-gbq (⭐458) - pandas Google Big Query.

• pysparkling (⭐268) - A pure Python implementation of Apache Spark's RDD and DStream interfaces.

• modin (⭐10k) - Speed up your pandas workflows by changing a single line of code.

Data Manipulation / Pipelines

• pandas-ply (⭐197) - Functional data manipulation for pandas.

• Dplython (⭐764) - Dplyr for Python.

• sklearn-pandas (⭐2.8k) - pandas integration with sklearn.

• pyjanitor (⭐1.4k) - Clean APIs for data cleaning.

Experimentation / Synthetic Data

• Sacred (⭐4.3k) - A tool to help you configure, organize, log, and reproduce experiments.

• Ax (⭐2.4k) - Adaptive Experimentation Platform.

Computations / Synthetic Data

• Dask (⭐13k) - Parallel computing with task scheduling.

Spatial Analysis / Synthetic Data

• GeoPandas (⭐4.6k) - Python tools for geographic data.

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