Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

Data Science Best Practices with pandas

Multiphysics Object Oriented Simulation Environment

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

Repository for models, descriptions, and software supporting the 2019 Workshop

🎓 Path to a free self-taught education in Computer Science!

Perform data science on data that remains in someone else's server

SOON TO BE DEPRECATED - Private machine learning progress

Deep universal probabilistic programming with Python and PyTorch

Cadence is a distributed, scalable, durable, and highly available orchestration engine to execute asynchronous long-running business logic in a scalable and resilient way.

A modern LaTeX Beamer theme

This is an unofficial LaTeX Beamer presentation template for Stanford University.

Gemini is a modern LaTex beamerposter theme 🖼

Data, Benchmarks, and methods submitted to the M4 forecasting competition

Includes the source code of the methods which participated in the M4 Competition

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

Tutorials and training material for the H2O Machine Learning Platform

Presentations from H2O meetups & conferences by the H2O.ai team

Notebooks that replicate original quantitative finance papers from Emanuel Derman

Talk Materials for SatRday Los Angeles

An implementation of our RA-L work 'Real-world Multi-object, Multi-grasp Detection'

OpenMM is a toolkit for molecular simulation using high performance GPU code.

Hacking structural biology with Python and pandas

Quantitative Interview Preparation Guide, updated version here ==>

A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.

An Interview Primer for Quantitative Finance

A complete computer science study plan to become a software engineer.

来自硅谷的终身学习大学

Turi Create simplifies the development of custom machine learning models.