[验证码识别-训练] This project is based on CNN/ResNet/DenseNet+GRU/LSTM+CTC/CrossEntropy to realize verification code identification. This project is only for training the model.

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the documentaion.

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

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

CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

C++ implementation of LSTM (Long Short Term Memory), in Kaldi's nnet1 framework. Used for automatic speech recognition, possibly language modeling etc, the training can be switched between CPU and GPU(CUDA). This repo is now merged into official Kaldi codebase(Karel's setup), so this repo is no longer maintained, please check out the Kaldi project instead.

对豆瓣影评进行文本分类情感分析，利用爬虫豆瓣爬取评论，进行数据清洗，分词，采用BERT、CNN、LSTM等模型进行训练，采用tensorboardX可视化训练过程，自然语言处理项目\A project for text classification, based on torch 1.7.1

This is a project for predicting air pollutants in London by time series model, including lstm, bilstm, Convlstm, attention lstm, lightGBM and ARIMA

The project focused on "Battery Remaining Useful Life (RUL) Prediction using a Data-Driven Approach with a Hybrid Deep Model combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM)." This repository aims to revolutionize battery health estimation by leveraging the power of deep learning to predict the remaining useful life

A Reinforcement Learning Project using PPO + LSTM

This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.

This project's aim, is to explore the world of Natural Language Processing (NLP) by building what is known as a Sentiment Analysis Model. We will be implementing and comparing both a Naïve Bayes and a Deep Learning LSTM model.

This project aims to predict VOLATILITY S&P 500 (^VIX) time series using LSTM.

Violence detection in videos using Deep Learning (CNNs + LSTMs). 98.5% video accuracy and 97.81% frame level accuracy (with threshold=3) was achieved through the proposed model by Joshua on HockeyFight dataset. Joshua's project was extended with real-time predictions on video feed coming from camera. Moreover, notebook is added to easily setup and run code on Google Colab.

This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.

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

This project aims to predict the hourly electricity load in Toronto based on the loads of previous 23 hours using LSTM recurrent neural network.

A bidirectional encoder-decoder LSTM neural network is trained for text summarization on the cnn/dailymail dataset. (MIT808 project)

This project forecasts renewable energy demand using LSTM-based time series models. It processes historical demand data, trains predictive models, and visualizes future trends, enabling better planning and management

This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron

A project that trains a LSTM recurrent neural network over a data-set of MIDI files.

This Project is predicting stocks for 32 companies with error less than 1% using LSTM Networks.

This project includes preprocessing of APNEA-ECG database and a LSTM-RNN model for per-segment OSA detection.

Predicting solar energy using machine learning (LSTM, PCA, boosting). This is our CS 229 project from autumn 2017. Report and poster are included.

The project uses EEG signals from the DEAP Dataset to classify emotions into 4 classes using Ensembled 1-D CNNs, LSTMs and 2D , 3D CNNs and Cascaded CNNs with 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.

Projects include the application of transfer learning to build a convolutional neural network (CNN) that identifies the artist of a painting, the building of predictive models for Bitcoin price data using Long Short-Term Memory recurrent neural networks (LSTMs) and a tutorial explaining how to build two types of neural network using as input the MNIST dataset, namely, a CNN using Keras and a fully-connected network using TensorFlow.