Image Caption GeneratorA neural network to generate captions for an image using CNN and RNN with BEAM Search.
Stars: ✭ 126 (+5.88%)
Pytorch Pos TaggingA tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.
Stars: ✭ 96 (-19.33%)
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
Stars: ✭ 2,943 (+2373.11%)
Pytorch Sentiment AnalysisTutorials on getting started with PyTorch and TorchText for sentiment analysis.
Stars: ✭ 3,209 (+2596.64%)
Rnn NotebooksRNN(SimpleRNN, LSTM, GRU) Tensorflow2.0 & Keras Notebooks (Workshop materials)
Stars: ✭ 48 (-59.66%)
Chinese Chatbot中文聊天机器人,基于10万组对白训练而成,采用注意力机制,对一般问题都会生成一个有意义的答复。已上传模型,可直接运行,跑不起来直播吃键盘。
Stars: ✭ 124 (+4.2%)
Image Caption Generator[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow
Stars: ✭ 141 (+18.49%)
DeepseqslamThe Official Deep Learning Framework for Route-based Place Recognition
Stars: ✭ 49 (-58.82%)
Rnn ctcRecurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.
Stars: ✭ 220 (+84.87%)
Document Classifier LstmA bidirectional LSTM with attention for multiclass/multilabel text classification.
Stars: ✭ 136 (+14.29%)
Gdax Orderbook MlApplication of machine learning to the Coinbase (GDAX) orderbook
Stars: ✭ 60 (-49.58%)
StylenetA cute multi-layer LSTM that can perform like a human 🎶
Stars: ✭ 187 (+57.14%)
Pytorch Seq2seqTutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Stars: ✭ 3,418 (+2772.27%)
Stock Price PredictorThis project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.
Stars: ✭ 146 (+22.69%)
SockeyeSequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
Stars: ✭ 990 (+731.93%)
sgrnnTensorflow implementation of Synthetic Gradient for RNN (LSTM)
Stars: ✭ 40 (-66.39%)
Lstm chemImplementation of the paper - Generative Recurrent Networks for De Novo Drug Design.
Stars: ✭ 87 (-26.89%)
StockpricepredictionStock Price Prediction using Machine Learning Techniques
Stars: ✭ 700 (+488.24%)
Nmt KerasNeural Machine Translation with Keras
Stars: ✭ 501 (+321.01%)
ThesemicolonThis repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
Stars: ✭ 345 (+189.92%)
Eeg DlA Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
Stars: ✭ 165 (+38.66%)
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 (+1662.18%)
Abstractive SummarizationImplementation of abstractive summarization using LSTM in the encoder-decoder architecture with local attention.
Stars: ✭ 128 (+7.56%)
Rnn For Joint NluPytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)
Stars: ✭ 176 (+47.9%)
Load forecastingLoad forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models
Stars: ✭ 160 (+34.45%)
Video ClassificationTutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101
Stars: ✭ 543 (+356.3%)
TelemanomA framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
Stars: ✭ 589 (+394.96%)
Deep Learning Time SeriesList of papers, code and experiments using deep learning for time series forecasting
Stars: ✭ 796 (+568.91%)
tiny-rnnLightweight C++11 library for building deep recurrent neural networks
Stars: ✭ 41 (-65.55%)
LstmvisVisualization Toolbox for Long Short Term Memory networks (LSTMs)
Stars: ✭ 959 (+705.88%)
Da Rnn📃 **Unofficial** PyTorch Implementation of DA-RNN (arXiv:1704.02971)
Stars: ✭ 256 (+115.13%)
sequence-rnn-pySequence analyzing using Recurrent Neural Networks (RNN) based on Keras
Stars: ✭ 28 (-76.47%)
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 (+132.77%)
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 (-52.94%)
datastories-semeval2017-task6Deep-learning model presented in "DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison".
Stars: ✭ 20 (-83.19%)
Pytorch Original TransformerMy implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.
Stars: ✭ 411 (+245.38%)
Char Rnn KerasTensorFlow implementation of multi-layer recurrent neural networks for training and sampling from texts
Stars: ✭ 40 (-66.39%)
SangitaA Natural Language Toolkit for Indian Languages
Stars: ✭ 43 (-63.87%)
Gym ContinuousdoubleauctionA custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
Stars: ✭ 50 (-57.98%)
Tensorflow Lstm SinTensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction
Stars: ✭ 52 (-56.3%)
Time AttentionImplementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971
Stars: ✭ 52 (-56.3%)