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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sequence-rnn-pySequence analyzing using Recurrent Neural Networks (RNN) based on Keras
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danifojo-2018-repeatrnnComparing Fixed and Adaptive Computation Time for Recurrent Neural Networks
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DeepseqslamThe Official Deep Learning Framework for Route-based Place Recognition
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Pytorch Sentiment AnalysisTutorials on getting started with PyTorch and TorchText for sentiment analysis.
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Codegan[Deprecated] Source Code Generation using Sequence Generative Adversarial Networks
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EasyesnPython library for Reservoir Computing using Echo State Networks
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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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sgrnnTensorflow implementation of Synthetic Gradient for RNN (LSTM)
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Rnn From ScratchUse tensorflow's tf.scan to build vanilla, GRU and LSTM RNNs
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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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lstm harLSTM based human activity recognition using smart phone sensor dataset
Stars: ✭ 20 (+25%)
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.
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RmdlRMDL: Random Multimodel Deep Learning for Classification
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Theano Kaldi RnnTHEANO-KALDI-RNNs is a project implementing various Recurrent Neural Networks (RNNs) for RNN-HMM speech recognition. The Theano Code is coupled with the Kaldi decoder.
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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.
Stars: ✭ 2,097 (+13006.25%)
IseebetteriSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
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VariationalNeuralAnnealingA variational implementation of classical and quantum annealing using recurrent neural networks for the purpose of solving optimization problems.
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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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tiny-rnnLightweight C++11 library for building deep recurrent neural networks
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Pytorch Pos TaggingA tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.
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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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ACTAlternative approach for Adaptive Computation Time in TensorFlow
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deep-blueberryIf you've always wanted to learn about deep-learning but don't know where to start, then you might have stumbled upon the right place!
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nemesystGeneralised and highly customisable, hybrid-parallelism, database based, deep learning framework.
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LSTM-footballMatchWinnerThis repository contains the code for a conference paper "Predicting the football match winner using LSTM model of Recurrent Neural Networks" that we wrote
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TCN-TFTensorFlow Implementation of TCN (Temporal Convolutional Networks)
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SynThaiThai Word Segmentation and Part-of-Speech Tagging with Deep Learning
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udacity-cvnd-projectsMy solutions to the projects assigned for the Udacity Computer Vision Nanodegree
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dl-for-harOfficial GitHub page of the best-paper award publication "Improving Deep Learning for HAR with shallow LSTMs" presented at the International Symposium on Wearable Computers 21' (ISWC 21')
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har-wisdm-lstm-rnnsHuman Activity Recognition on the Wireless Sensor Data Mining (WISDM) dataset using LSTM Recurrent Neural Networks
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STAR Network[PAMI 2021] Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
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Clockwork-RNNThis repository is a reproduction of the clockwork RNN paper.
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TF-Speech-Recognition-Challenge-SolutionSource code of the model used in Tensorflow Speech Recognition Challenge (https://www.kaggle.com/c/tensorflow-speech-recognition-challenge). The solution ranked in top 5% in private leaderboard.
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Awesome-Human-Activity-RecognitionAn up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources. Please note that most of the collections of researches are mainly based on IMU data.
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neural-namerFantasy name generator in TensorFlow
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Seq2Seq-chatbotTensorFlow Implementation of Twitter Chatbot
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rnn-theanoRNN(LSTM, GRU) in Theano with mini-batch training; character-level language models in Theano
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Market-Trend-PredictionThis is a project of build knowledge graph course. The project leverages historical stock price, and integrates social media listening from customers to predict market Trend On Dow Jones Industrial Average (DJIA).
Stars: ✭ 57 (+256.25%)
presidential-rnnProject 4 for Metis bootcamp. Objective was generation of character-level RNN trained on Donald Trump's statements using Keras. Also generated Markov chains, and quick pyTorch RNN as baseline. Attempted semi-supervised GAN, but was unable to test in time.
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unicornnOfficial code for UnICORNN (ICML 2021)
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