Trained models with fast variant of the "best" LSTM models + legacy models

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

Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.

Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting

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).

Designing end-to-end weekly stock report generation using LSTM and Agentic AI. Deploying on AWS with MLOps practices.

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

Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.

Train Tesseract LSTM with make

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network