🧙 Build, run, and manage data pipelines for integrating and transforming data.

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

120+ interactive Python coding interview challenges (algorithms and data structures). Includes Anki flashcards.

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

A PyTorch Library for Meta-learning Research

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Code for Continual Learning of Context-dependent Processing in Neural Networks

Numenta Platform for Intelligent Computing PyTorch libraries

A Literate Program about Data Structures and Object-Oriented Programming

Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic

18.330 Introduction to Numerical Analysis

This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaof…

Home work in python using cvxpy to Stephen Boyd's Convex Optimization class (CVX101 Stanford)

Numenta Platform for Intelligent Computing PyTorch libraries

Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

The source code for "An Actor Critic Algorithm for Structured Prediction"

Code for "Systematic Generalization: What Is Required and Can It Be Learned"

pandas中文教程

Advanced Julia for undergraduate physicists

Advanced Topics in Scientific Computing with Julia

18.S096 three-week course at MIT

Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

18.S096 - Applications of Scientific Machine Learning

Data Science at the Command Line

Source code for my collection of articles on using pandas.

Observations from Ian on successfully delivering data science products

The 3rd edition of course.fast.ai

Configuration files for my linux/mac.

Deep Learning and Reinforcement Learning Library for Developers and Scientists