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

GitHub Action for repo2docker

公众号“无痛入门音乐科技”开源代码

Faster-RCNN based on Densecap(deprecated)

Models and Pipelines for the Spark NLP library

Code for reproducing the results from the paper Few Shot Text Classification with a Human in the Loop

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

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

p5_VehicleDetection_Unet

FCOS: Fully Convolutional One-Stage Object Detection.

PyTorch tutorials A to Z

MAGNet: Multi-agents control using Graph Neural Networks

Código para el curso "Aprende Data Science y Machine Learning con Python"

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

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).