gru

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

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.

In PyTorch Learing Neural Networks Likes CNN、BiLSTM

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

Predict Cryptocurrency Price with Deep Learning

Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

Neural Machine Translation with Keras

百度云魅族深度学习应用大赛

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST

Keras tutorial for beginners (using TF backend)

Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

Haste: a fast, simple, and open RNN library

🔬 Nano size Theano LSTM module

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

Recurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.

OpenTextClassification is all you need for text classification! Open text classification for everyone, enjoy your NLP journey! 这可能是目前为止最全面的开源文本分类项目，支持中英双语、多种模型、多种任务。

This is the end-to-end Speech Recognition neural network, deployed in Keras. This was my final project for Artificial Intelligence Nanodegree @Udacity.

CryptoCurrency prediction using machine learning and deep learning