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🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.

Resources for weekly Data Science Lunch & Learns

Lazydata: Scalable data dependencies for Python projects

Data processing and modelling framework for automating tasks (incl. Python & SQL transformations).

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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

A Java Toolbox for Scalable Probabilistic Machine Learning

Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark.

ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.

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A collection of Jupyter/IPython notebooks

Statistical Machine Intelligence & Learning Engine

An implementation of "Network Representation Learning with Rich Text Information" (IJCAI '15).

Easy pipelines for pandas DataFrames.

danfo.js is an open source, JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.

A list of awesome and proven Artificial Intelligence use cases and applications

Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

🚘 "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.

A site that displays up to date COVID-19 stats, powered by fastpages.

A graph-based functional API for building complex scikit-learn pipelines.

Best Practices, code samples, and documentation for Computer Vision.

This is a repository of a topic-centric public data sources in high quality for Recommender Systems (RS)

Human Activity Recognition (HAR) with 1D Convolutional Neural Network in Python and Keras

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

Android library to draw beautiful and rich line charts.

Practical Data Science with Ruby based tools.

Example projects, code, and documents for comparing Mathematica with R.

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

VIP cheatsheets for Stanford's CS 230 Deep Learning

Robust, distributed version control for large files.

A list of Twitter datasets and related resources.

NeuralPy: A Keras like deep learning library works on top of PyTorch

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

All of my recommendations for aspiring engineers in a single place, coming from various areas of interest.

Rumale is a machine learning library in Ruby

Datasets, tools, and benchmarks for representation learning of code.

The 3rd edition of course.fast.ai

A collection of computer vision pre-trained models.

Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data

数据挖掘开源书

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A set of useful tools for competitive data science.

fklearn: Functional Machine Learning

The (un)official repository for my master thesis

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Course materials for Dartmouth Course: Storytelling with Data (PSYC 81.09).

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