931 Open source projects that are alternatives of or similar to Tensorflow Lstm Sin

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

Some of the ML and DL related reading materials, research papers that I've read

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.

Application of machine learning to the Coinbase (GDAX) orderbook

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

Tensorflow Implementation of Recurrent Neural Network (Vanilla, LSTM, GRU) for Text Classification

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

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

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

Predict time-series with one line of code.

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Implementing different RNN models (LSTM,GRU) & Convolution models (Conv1D, Conv2D) on a subset of Amazon Reviews data with TensorFlow on Python 3. A sentiment analysis project.

Time series based anomaly detector

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

Malicious URL detector using keras recurrent networks and scikit-learn classifiers

THEANO-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.

TensorFlow implementation of multi-layer recurrent neural networks for training and sampling from texts

light deep neural network tools box(LSTM,GRU,RNN,CNN,Bi-LSTM,etc)

Video prediction using lstm and unet

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

[AAAI 2020] Modeling Personality with Attentive Networks and Contextual Embeddings

The Official Deep Learning Framework for Route-based Place Recognition

Keras and PyTorch implementations of the MaLSTM model for computing Semantic Similarity.

language models toolkits with hierarchical softmax setting

The project uses state of the art deep learning on collected data for automatic analysis of emotions.

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

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

Keras implementation of video classifier

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

Extending broom for time series forecasting

Anomaly detection for streaming data using autoencoders

Recurrent Additive Networks for Tensorflow

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

Deep-learning model presented in "DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison".

bitcoin prediction algorithms

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).

Using LSTM network for time series forecasting

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

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

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST

🥕 Evolutionary Neural Networks in JavaScript

基於關聯式新聞提取方法之雙階段注意力機制模型用於股票預測

ARIMA, SARIMA, SARIMAX and AutoARIMA models for time series analysis and forecasting in the browser and Node.js

Deep Learning

Lightweight C++11 library for building deep recurrent neural networks

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

🔬 Nano size Theano LSTM module

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

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

RNNSharp 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.

PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

Keras tutorial for beginners (using TF backend)

Predict Cryptocurrency Price with Deep Learning

Named Entity Recognition using multilayered bidirectional LSTM

Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

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.

Anomaly detection for temporal data using LSTMs