1101 Open source projects that are alternatives of or similar to ConvLSTM-PyTorch

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

A Keras library for multi-step time-series forecasting.

pytorch-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.

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Recurrent Neural Networks (RNN, GRU, LSTM) and their Bidirectional versions (BiRNN, BiGRU, BiLSTM) for word & character level language modelling in Theano

RNN and general weights, gradients, & activations visualization in Keras & TensorFlow

CNN, RNN, and Bayesian NN classification for ECG time-series (using TensorFlow in Swift and Python)

A 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.

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

Word Embedding + LSTM + FC

Part-of-Speech Tagger and custom implementations of LSTM, GRU and Vanilla RNN

Deep Learning

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

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

Recurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.

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

Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)

Keras tutorial for beginners (using TF backend)

Haste: a fast, simple, and open RNN library

Recurrent Additive Networks for Tensorflow

Nested LSTM Cell

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.

Electric load forecast using Long-Short-Term-Memory (LSTM) recurrent neural network

Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data. Human Activity Recognition Challenge. Springer SIST (2020)

Deep learning driven jazz generation using Keras & Theano!

Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs

Sequence analyzing using Recurrent Neural Networks (RNN) based on Keras

Efficient, transparent deep learning in hundreds of lines of code.

A modular library built on top of Keras and TensorFlow to generate a caption in natural language for any input image.

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

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

Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

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

A stock trading bot that uses machine learning to make price predictions.

Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

🌊 Numerically solving and backpropagating through the wave equation

RNN based Time-series Anomaly detector model implemented in Pytorch.

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

List of papers, code and experiments using deep learning for time series forecasting

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

Annotation of ECG signals using deep learning, tensorflow’ Keras

GANs for time series generation in pytorch

RNN(LSTM, GRU) in Theano with mini-batch training; character-level language models in Theano

Anomaly detection for temporal data using LSTMs

Convolutional GRU

Official 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

5th place solution for Kaggle competition Favorita Grocery Sales Forecasting

[PAMI 2021] Gating Revisited: Deep Multi-layer RNNs That Can Be Trained

Detecting and Predicting Forest Cover Change in Pakistani Areas Using Remote Sensing Imagery

LSTM based human activity recognition using smart phone sensor dataset

Hands-on in-person workshop for Deep Learning with TensorFlow

Speaker 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).

Library of autoencoders for sequential data

My solutions to the projects assigned for the Udacity Computer Vision Nanodegree

Rainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.

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

Online Hand Writing Recognition using BLSTM