Lstm Human Activity RecognitionHuman 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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Mutual labels: recurrent-neural-networks, lstm, rnn
Pytorch Pos TaggingA tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.
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Mutual labels: recurrent-neural-networks, lstm, rnn
RnnsharpRNNSharp 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.
Stars: ✭ 277 (+575.61%)
Mutual labels: recurrent-neural-networks, lstm, rnn
DeepseqslamThe Official Deep Learning Framework for Route-based Place Recognition
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Mutual labels: recurrent-neural-networks, lstm, rnn
Rnn ctcRecurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.
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sgrnnTensorflow implementation of Synthetic Gradient for RNN (LSTM)
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Mutual labels: recurrent-neural-networks, lstm, rnn
Bitcoin Price Prediction Using LstmBitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network
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automatic-personality-prediction[AAAI 2020] Modeling Personality with Attentive Networks and Contextual Embeddings
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Mutual labels: recurrent-neural-networks, lstm, rnn
Pytorch Kaldipytorch-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.
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Mutual labels: recurrent-neural-networks, lstm, rnn
Linear Attention Recurrent Neural NetworkA 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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Mutual labels: recurrent-neural-networks, lstm, rnn
Pytorch Learners TutorialPyTorch tutorial for learners
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SpeakerDiarization RNN CNN LSTMSpeaker Diarization is the problem of separating speakers in an audio. There could be any number of speakers and final result should state when speaker starts and ends. In this project, we analyze given audio file with 2 channels and 2 speakers (on separate channels).
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Pytorch Sentiment AnalysisTutorials on getting started with PyTorch and TorchText for sentiment analysis.
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sequence-rnn-pySequence analyzing using Recurrent Neural Networks (RNN) based on Keras
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Speech-RecognitionEnd-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
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ConvLSTM-PyTorchConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST
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theano-recurrenceRecurrent Neural Networks (RNN, GRU, LSTM) and their Bidirectional versions (BiRNN, BiGRU, BiLSTM) for word & character level language modelling in Theano
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CS231nPyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"
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myDLDeep Learning
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