Deep learning driven jazz generation using Keras & Theano!
Har Stacked Residual Bidir Lstms
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Efficient, transparent deep learning in hundreds of lines of code.
A modular library built on top of Keras and TensorFlow to generate a caption in natural language for any input image.
Ntm One Shot Tf
One Shot Learning using Memory-Augmented Neural Networks (MANN) based on Neural Turing Machine architecture in Tensorflow
A simple effective ToolKit for short text matching
Recurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.
Reasoning Over Knowledge Graph Paths for Recommendation
Siamese LSTM for evaluating semantic similarity between sentences of the Quora Question Pairs Dataset.
Haste: a fast, simple, and open RNN library
End-2-end speech synthesis with recurrent neural networks
Screenshot To Code
A neural network that transforms a design mock-up into a static website.
Icdar 2019 Sroie
ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction
Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
Up Down Captioner
Automatic image captioning model based on Caffe, using features from bottom-up attention.
Char Rnn Chinese
Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch. Based on code of https://github.com/karpathy/char-rnn. Support Chinese and other things.
A cute multi-layer LSTM that can perform like a human 🎶
Datastories Semeval2017 Task4
Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis".
Rnn For Joint Nlu
Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading
Machine Learning Is All You Need
🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
Relation Classification Using Bidirectional Lstm Tree
TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations
Accel Brain Code
The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.
A visualization tool for understanding and debugging RNNs
Library of autoencoders for sequential data
Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models
Keras implementation of Legendre Memory Units
🤖 Neural SPARQL Machines for Knowledge Graph Question Answering.