Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
-
Updated
Jan 20, 2024 - Jupyter Notebook
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
Predict Cryptocurrency Price with Deep Learning
Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
Keras tutorial for beginners (using TF backend)
百度云魅族深度学习应用大赛
Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow
Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models
Neural Machine Translation with Keras
🔬 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 t…
This is the end-to-end Speech Recognition neural network, deployed in Keras. This was my final project for Artificial Intelligence Nanodegree @udacity.
Recurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.
ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST
Solve your natural language processing problems with smart deep neural networks
Tensorflow Implementation of Recurrent Neural Network (Vanilla, LSTM, GRU) for Text Classification
RNN(LSTM, GRU) in Theano with mini-batch training; character-level language models in Theano
A Keras library for multi-step time-series forecasting.
Add a description, image, and links to the gru topic page so that developers can more easily learn about it.
To associate your repository with the gru topic, visit your repo's landing page and select "manage topics."