be-redAsmara's repositories
lowresource-nlp-bootcamp-2020
The website for the CMU Language Technologies Institute low resource NLP bootcamp 2020
graph-based-deep-learning-literature
links to conference publications in graph-based deep learning
PySyft
A library for encrypted, privacy preserving machine learning
Awesome-Mobility-Machine-Learning-Contents
Machine Learning / Deep Learning Contents in Mobility Industry(Transportation)
lara2018
This repository is intended to develop the work supported by the Latin America Research Awards 2018.
tydiqa
TyDi QA contains 200k human-annotated question-answer pairs in 11 Typologically Diverse languages, written without seeing the answer and without the use of translation, and is designed for the training and evaluation of automatic question answering systems. This repository provides evaluation code and a baseline system for the dataset.
pytorch-Deep-Learning
Deep Learning (with PyTorch)
3D-Machine-Learning
A resource repository for 3D machine learning
devops-master-class
Learn Devops with Docker, Kubernetes, Terraform, Ansible, Jenkins and Azure Devops
probnmn-clevr
Code for ICML 2019 paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" [long-oral]
Referring-Expression-Generation-and-Comprehension-
Research Paper
nn4nlp-code
Code Samples from Neural Networks for NLP
learn
Neuro-symbolic interpretation learning (mostly just language-learning, for now)
Distracted-Driver-Detection
Final Report
IROS2019-paper-list
IROS2019 paper list from PaopaoRobot
pytorch-seq2seq
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. [IN PROGRESS]
awesome-multimodal-ml
Reading list for research topics in multimodal machine learning
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
gans-in-action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
NSCL-PyTorch-Release
PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
machine_learning_examples
A collection of machine learning examples and tutorials.
SDE
Source code for the paper "Multilingual Neural Machine Translation with Soft Decoupled Encoding"
crop_yield_prediction
Crop Yield Prediction with Deep Learning
Deep-Learning-Cheat-Sheets
Cheat Sheet - RNN and CNN
abstractive-text-summarization
PyTorch implementation/experiments on Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond paper.
cs581-database-management
This project evaluates ride-sharing algorithms on spatio-temporal data. The data in this case represents nearly 700 million trips in New York City.
DAgger
Reinforcement Learning -- Imitation Learning, Behavior Cloning, DAgger (Data Aggregation)
speaker_listener_reinforcer
Torch Implementation of Speaker-Listener-Reinforcer for Referring Expression Generation and Comprehension