#Lstm
Showing 60 of 215 repositories tagged #lstm, ranked by stars
Tesseract Open Source OCR Engine (main repository)
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
A neural network that transforms a design mock-up into a static website.
Machine learning, in numpy
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Friendly machine learning for the web! 🤖
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
Deep learning driven jazz generation using Keras & Theano!
End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
List of papers, code and experiments using deep learning for time series forecasting
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 BER
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.
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
Use unsupervised and supervised learning to predict stocks
NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.
TensorFlow template application for deep learning
Self-contained Machine Learning and Natural Language Processing library in Go
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0
Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.
Stock Price Prediction using Machine Learning Techniques
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
A tool for converting ONNX files to LiteRT/TFLite/TensorFlow, PyTorch native code (nn.Module), TorchScript (.pt), state_dict (.pt), Exported Program (.pt2), and Dynamo ONNX. It also supports direct conversion from LiteRT to PyTorch.
List of Molecular and Material design using Generative AI and Deep Learning
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall
Chatbot in 200 lines of code using TensorLayer
A simple neural network for python autocompletion
Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting
A stock trading bot that uses machine learning to make price predictions.
Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models
Neural Turing Machines (NTM) - PyTorch Implementation
Hierarchical Attention Networks for Document Classification in PyTorch
Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019)
Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.
DanceNet -💃💃Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)
Implementation of a hierarchical CNN based model to detect Big Five personality traits
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
This repo contains the updated version of all the assignments/labs (done by me) of Deep Learning Specialization on Coursera by Andrew Ng. It includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc.
Lightweight and Interpretable ML Model for Speech Emotion Recognition and Ambiguity Resolution (trained on IEMOCAP dataset)
Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).
Keras tutorial for beginners (using TF backend)
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot
The practitioner's forecasting library
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。
A LSTM model using Risk Estimation loss function for stock trades in market
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.
🎭 Sentiment Analysis of Twitter data using combined CNN and LSTM Neural Network models
Music genre classification with LSTM Recurrent Neural Nets in Keras & PyTorch
:microscope: Nano size Theano LSTM module
Efficient, transparent deep learning in hundreds of lines of code.
Predict Race and Ethnicity Based on the Sequence of Characters in a Name
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading