plotly / Dash Wind Streaming

Models and Pipelines for the Spark NLP library

Game Theory and Python, a workshop investigating repeated games using the prisoner's dilemma

GitHub Action for repo2docker

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).

Code for my EMNLP 2018 paper "SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach"

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

Multi-purpose tool for sound design and music production implemented in TensorFlow.

MAGNet: Multi-agents control using Graph Neural Networks

p5_VehicleDetection_Unet

DEPRECATED: please see individual lesson repositories for current material.

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

Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

capsule networks that achieves outstanding performance on the German traffic sign dataset

PyTorch tutorials A to Z

FCOS: Fully Convolutional One-Stage Object Detection.

机器学习与深度学习算法示例

公众号“无痛入门音乐科技”开源代码