🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…

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,…

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

A collection of machine learning examples and tutorials.

Official Matplotlib cheat sheets

A library of extension and helper modules for Python's data analysis and machine learning libraries.

N-D labeled arrays and datasets in Python

Turn repositories into Jupyter-enabled Docker images

Instant access to many datasets in Python.

ML-Ensemble – high performance ensemble learning

matplotlib extension for vaporwave aesthetics

Autoencoder network for learning a continuous representation of molecular structures.

Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.

The easy way to write your own flavor of Pandas

mglearn helper package for "Introduction to Machine Learning with Python"

Code from the article "Drawing Good-looking Trees" in Python Magazine