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.

Text classification using deep learning models in Pytorch

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch

A R/Shiny app for interactive RNN tensorflow models

I used in this project a reccurent neural network to generate c code based on a dataset of c files from the linux repository.

generate captions for images using a CNN-RNN model that is trained on the Microsoft Common Objects in COntext (MS COCO) dataset

Recurrent Neural Network (LSTM) by using TensorFlow and Keras in Python for BitCoin price prediction

Sinhalese Language based Hate Speech Detection

Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.

A food ordering android application with feedback analyzer to improve food suggestions to customer.

Annabelle the noob. An experimental, incomplete(at present) chatbot.

基于tensorflow搭建的神经网络recursive autuencode，用于实现句子聚类

make stock prediction model using Tensorflow, Python and web crawling

Time series forecasting using RNN, Twitter Sentiment Analysis and Turtle Trading Strategy applied on Cryptocurrency

A repository for my pytorch models

Piano music generation with RNN with Tensorflow

Analyze and classify videos in the UCF101 dataset

An AI chatbot built using SEQ2SEQ Model

kerasのRNNでEnigmaを解きます

Built RNNs that can generate sequences based on input data - with a focus on two applications: used real market data in order to predict future Apple stock prices using an RNN model. The second one will be trained on Sir Arthur Conan Doyle's classic novel Sherlock Holmes and generates wacky sentences based on it that may - or may not - become the next great Sherlock Holmes novel.

Character-level Recurrent Neural Network model

RNN based on LSTM

An attention mechanism implementation based on keras with theano as backend.

A QA System based by BiLSTM. 一个基于双向LSTM的智能问答系统，有交互界面、详细注释、多版本、能运行、文件齐全。

RNN for Time-Series Stock Prediction and Text Generation

Used re-constructed news title data to predict stock by deep learning(LSTM).This is the final project of NTUST CS5144701 Practices of Deep Learning course.

Cross-Topic Quotes Generation using Word Embeddings

Simple music generation rnn. Update/Built untop of MattVitelli/GRUV and unnati-xyz/music-generation