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.

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Text Classification Cnn Rnn

CNN-RNN中文文本分类，基于TensorFlow

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Deeplearning.ai Assignments

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Tensorflow Tutorial

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

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Rmdl

RMDL: Random Multimodel Deep Learning for Classification

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Multi Class Text Classification Cnn Rnn

Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.

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Lstm Human Activity Recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

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Tensorflow cookbook

Code for Tensorflow Machine Learning Cookbook

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Stockpriceprediction

Stock Price Prediction using Machine Learning Techniques

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Ad examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

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Linear Attention Recurrent Neural Network

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

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Lstm peptides

Long short-term memory recurrent neural networks for learning peptide and protein sequences to later design new, similar examples.

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Rnn Notebooks

RNN(SimpleRNN, LSTM, GRU) Tensorflow2.0 & Keras Notebooks (Workshop materials)

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sgrnn

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

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Gru Svm

[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection

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Hred Attention Tensorflow

An extension on the Hierachical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion, our implementation is in Tensorflow and uses an attention mechanism.

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Bitcoin Price Prediction Using Lstm

Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

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Pytorch-POS-Tagger

Part-of-Speech Tagger and custom implementations of LSTM, GRU and Vanilla RNN

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Time Attention

Implementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971