Displaying 1 to 12 from 12 results

theano_lstm - :microscope: Nano size Theano LSTM module

  •    Python

Implements most of the great things that came out in 2014 concerning recurrent neural networks, and some good optimizers for these types of networks. This module also contains the SGD, AdaGrad, and AdaDelta gradient descent methods that are constructed using an objective function and a set of theano variables, and returns an updates dictionary to pass to a theano function.

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

  •    Jupyter

Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

tensorflow-lstm-sin - TensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction

  •    Python

Single- and multilayer LSTM networks with no additional output nonlinearity based on aymericdamien's TensorFlow examples and Sequence prediction using recurrent neural networks. Experiments with varying numbers of hidden units, LSTM cells and techniques like gradient clipping were conducted using static_rnn and dynamic_rnn. All networks have been optimized using Adam on the MSE loss function.

rnn-theano - RNN(LSTM, GRU) in Theano with mini-batch training; character-level language models in Theano

  •    Python

RNN(LSTM, GRU) in Theano with mini-batch training; character-level language models in Theano

tf-ran-cell - Recurrent Additive Networks for Tensorflow

  •    Python

This is a implementation of Recurrent Additive Networks that extends Tensorflow's RNNCell.

SUDL - light deep neural network tools box(LSTM,GRU,RNN,CNN,Bi-LSTM,etc)

  •    C++

net architecture is built by proto file that you defined, just like what the examples do.

pytorch-kaldi - pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems

  •    Perl

pytorch-kaldi is a public repository 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. The provided solution is designed for large-scale speech recognition experiments on both standard machines and HPC clusters.

theano-kaldi-rnn - THEANO-KALDI-RNNs is a project implementing various Recurrent Neural Networks (RNNs) for RNN-HMM speech recognition

  •    Perl

THEANO-KALDI-RNNs is a software which offers the possibility to use various Recurrent Neural Networks (RNNs) in the context of a Kaldi-based hybrid HMM/RNN speech recognizer. Note: A new project called "pytorch-kaldi" https://github.com/mravanelli/pytorch-kaldi is now available. If you are interested, please take a look into it.

Hierarchical-Attention-Network - Implementation of Hierarchical Attention Networks in PyTorch

  •    Jupyter

We know that documents have a hierarchical structure, words combine to form sentences and sentences combine to form documents. We can try to learn that structure or we can input this hierarchical structure into the model and see if it improves the performance of existing models. This paper exploits that structure to build a classification model. This is a (close) implementation of the model in PyTorch.

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