Original implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery" by Shion Honda et al.

https://plot.ly/dash/gallery/live-wind-data/

The Hacker Within at the University of California - Berkeley

MAGNet: Multi-agents control using Graph Neural Networks

This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.

Repository which contains various scripts and work with various basketball statistics

FCOS: Fully Convolutional One-Stage Object Detection.

机器学习与深度学习算法示例

This repository contains IPython notebooks that I have written.

AIND Term 2 -- VUI Capstone Project

公众号“无痛入门音乐科技”开源代码

Python for Finance (O'Reilly)

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

Exploring applications of deep learning to the world of wine