607 Open source projects that are alternatives of or similar to 5baa61e4c9b93f3f0682250b6cf8331b7ee68fd8

A PyTorch Tutorials of Sentiment Analysis Classification (RNN, LSTM, Bi-LSTM, LSTM+Attention, CNN)

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LSTM based human activity recognition using smart phone sensor dataset

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

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

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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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使用Theano实现的一些RNN代码，包括最基本的RNN，LSTM，以及部分Attention模型，如论文MLSTM等

This is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.

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

language model in Chinese，基于Pytorch官方文档实现

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

My Attempt(s) In The World Of ML/DL....

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.

CNN, LSTM, NBOW, fasttext 中文文本分类

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Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models

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

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Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch. Based on code of https://github.com/karpathy/char-rnn. Support Chinese and other things.

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End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

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Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

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