ECGClassifierCNN, RNN, and Bayesian NN classification for ECG time-series (using TensorFlow in Swift and Python)
Stars: ✭ 53 (+112%)
myDLDeep Learning
Stars: ✭ 18 (-28%)
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.
Stars: ✭ 31 (+24%)
Rnn ctcRecurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.
Stars: ✭ 220 (+780%)
ConvLSTM-PyTorchConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST
Stars: ✭ 202 (+708%)
Pytorch Seq2seqTutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Stars: ✭ 3,418 (+13572%)
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 (+8288%)
tf-ran-cellRecurrent Additive Networks for Tensorflow
Stars: ✭ 16 (-36%)
Gru Svm[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection
Stars: ✭ 76 (+204%)
HasteHaste: a fast, simple, and open RNN library
Stars: ✭ 214 (+756%)
rnn-theanoRNN(LSTM, GRU) in Theano with mini-batch training; character-level language models in Theano
Stars: ✭ 68 (+172%)
See RnnRNN and general weights, gradients, & activations visualization in Keras & TensorFlow
Stars: ✭ 102 (+308%)
Pytorch-POS-TaggerPart-of-Speech Tagger and custom implementations of LSTM, GRU and Vanilla RNN
Stars: ✭ 24 (-4%)
Rnn NotebooksRNN(SimpleRNN, LSTM, GRU) Tensorflow2.0 & Keras Notebooks (Workshop materials)
Stars: ✭ 48 (+92%)
ms-convSTAR[RSE21] Pytorch code for hierarchical time series classification with multi-stage convolutional RNN
Stars: ✭ 17 (-32%)
Eeg DlA Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
Stars: ✭ 165 (+560%)
RnnoiseRecurrent neural network for audio noise reduction
Stars: ✭ 2,266 (+8964%)
Load forecastingLoad forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models
Stars: ✭ 160 (+540%)
Skip Thoughts.torchPorting of Skip-Thoughts pretrained models from Theano to PyTorch & Torch7
Stars: ✭ 146 (+484%)
STAR Network[PAMI 2021] Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
Stars: ✭ 16 (-36%)
theano-recurrenceRecurrent Neural Networks (RNN, GRU, LSTM) and their Bidirectional versions (BiRNN, BiGRU, BiLSTM) for word & character level language modelling in Theano
Stars: ✭ 40 (+60%)
solar-forecasting-RNNMulti-time-horizon solar forecasting using recurrent neural network
Stars: ✭ 29 (+16%)
cnn-rnn-classifierA practical example on how to combine both a CNN and a RNN to classify images.
Stars: ✭ 47 (+88%)
VariationalNeuralAnnealingA variational implementation of classical and quantum annealing using recurrent neural networks for the purpose of solving optimization problems.
Stars: ✭ 21 (-16%)
char-VAEInspired by the neural style algorithm in the computer vision field, we propose a high-level language model with the aim of adapting the linguistic style.
Stars: ✭ 18 (-28%)
sequence-rnn-pySequence analyzing using Recurrent Neural Networks (RNN) based on Keras
Stars: ✭ 28 (+12%)
GRU AndroidAn RNN (GRU) TensorFlow Android App for word prediction.
Stars: ✭ 15 (-40%)
Solar-Rad-ForecastingIn these notebooks the entire research and implementation process carried out for the construction of various machine learning models based on neural networks that are capable of predicting levels of solar radiation is captured given a set of historical data taken by meteorological stations.
Stars: ✭ 24 (-4%)
DeepLearning-LabCode lab for deep learning. Including rnn,seq2seq,word2vec,cross entropy,bidirectional rnn,convolution operation,pooling operation,InceptionV3,transfer learning.
Stars: ✭ 83 (+232%)
awesome-speech-enhancementA curated list of awesome Speech Enhancement papers, libraries, datasets, and other resources.
Stars: ✭ 48 (+92%)
cnn-rnn-bitcoinReusable CNN and RNN model doing time series binary classification
Stars: ✭ 28 (+12%)
Deep-Learning-CourseraProjects from the Deep Learning Specialization from deeplearning.ai provided by Coursera
Stars: ✭ 123 (+392%)
ArrayLSTMGPU/CPU (CUDA) Implementation of "Recurrent Memory Array Structures", Simple RNN, LSTM, Array LSTM..
Stars: ✭ 21 (-16%)
stylegan-pokemonGenerating Pokemon cards using a mixture of StyleGAN and RNN to create beautiful & vibrant cards ready for battle!
Stars: ✭ 47 (+88%)
EBIM-NLIEnhanced BiLSTM Inference Model for Natural Language Inference
Stars: ✭ 24 (-4%)
GestureAIRNN(Recurrent Nerural network) model which recognize hand-gestures drawing 5 figures.
Stars: ✭ 20 (-20%)
Speech-RecognitionEnd-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
Stars: ✭ 21 (-16%)
LearningMetersPoemsOfficial repo of the article: Yousef, W. A., Ibrahime, O. M., Madbouly, T. M., & Mahmoud, M. A. (2019), "Learning meters of arabic and english poems with recurrent neural networks: a step forward for language understanding and synthesis", arXiv preprint arXiv:1905.05700
Stars: ✭ 18 (-28%)
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).
Stars: ✭ 56 (+124%)
mangoQuestion-Answering NLP model with character-level RNN (TensorFlow).
Stars: ✭ 15 (-40%)
air writingOnline Hand Writing Recognition using BLSTM
Stars: ✭ 26 (+4%)
IndRNN pytorchIndependently Recurrent Neural Networks (IndRNN) implemented in pytorch.
Stars: ✭ 112 (+348%)
DrowsyDriverDetectionThis is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.
Stars: ✭ 82 (+228%)
modulesThe official repository for our paper "Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks". We develop a method for analyzing emerging functional modularity in neural networks based on differentiable weight masks and use it to point out important issues in current-day neural networks.
Stars: ✭ 25 (+0%)