A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

Toturials coming with the "data science roadmap" picture.

TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。

Book about interpretable machine learning

The "Python Machine Learning (3rd edition)" book code repository

This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.

Homepage for STAT 157 at UC Berkeley

Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

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

Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping

bamboolib - a GUI for pandas DataFrames

Practical assignments of the Deep|Bayes summer school 2019

Face Image Motion Model (Photo-2-Video) based on "first-order-model" repository.

scrape flight info off of google flights and find cheapest destinations