Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
The most widely used Python to C compiler
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
The Open Source Feature Store for Machine Learning
Stream Framework is a Python library, which allows you to build news feed, activity streams and notification systems using Cassandra and/or Redis. The authors of Stream-Framework also provide a cloud service for feed technology:
Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.
Bare bone examples of machine learning in TensorFlow
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.
Example end to end data engineering project.
Workflows and interfaces for neuroimaging packages
Server for the ListenBrainz project, including the front-end (javascript/react) code that it serves and all of the data processing components that LB uses.
Numba extension for compiling Pandas data frames, Intel® Scalable Dataframe Compiler
Redis in a python module.
A SDK/library for decentralized web and distributing computing projects
Deploying complex solutions, magically.
PySpark Cheat Sheet - example code to help you learn PySpark and develop apps faster
The Signal Metadata Format Specification
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