7100 Open source projects that are alternatives of or similar to Bitcoin Price Prediction Using Lstm

PyTorch tutorial for learners

List of papers, code and experiments using deep learning for 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.

[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow

Anomaly detection for temporal data using LSTMs

Application of machine learning to the Coinbase (GDAX) orderbook

The Official Deep Learning Framework for Route-based Place Recognition

A 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)

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

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

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

Stock Price Prediction using Machine Learning Techniques

Keras tutorial for beginners (using TF backend)

Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems

The deeplearning algorithms implemented by tensorflow

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。

IEEE "Invited Talk on Deep Learning" 03/02/2018

Sentiment Analysis with LSTMs in Tensorflow

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

Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.

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

Natural Language Processing with TensorFlow, published by Packt

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

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

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

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

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

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

All about Neural Networks!

All my experiments with AI and ML

Chinese Poetry Generation

How to build RNNs and LSTMs from scratch with NumPy.

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

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.

Deep learning codes and projects using Python

A cute multi-layer LSTM that can perform like a human 🎶

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.

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

My bachelor's thesis—analyzing the application of LSTM-based RNNs on financial markets. 🤓

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).

Implementation of the paper - Generative Recurrent Networks for De Novo Drug Design.

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

Примеры для курса "Программирование глубоких нейронных сетей на Python"

A Natural Language Toolkit for Indian Languages

📃 **Unofficial** PyTorch Implementation of DA-RNN (arXiv:1704.02971)

This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.

RMDL: Random Multimodel Deep Learning for Classification

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

This is an open sourced book on deep learning.

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

Tutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101

Named Entity Recognition using multilayered bidirectional LSTM

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.

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

Sentiment Analysis on the First Republic Party debate in 2016 based on Python,NLTK and ML.

My Attempt(s) In The World Of ML/DL....

Neural machine translator for English2German translation.

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

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