Implementing Deep Learning Architectures for Advanced Machine Learning using PyTorch
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Implemented Deep Neural Network architectures using PyTorch for advanced machine learning applications. The repository contains two major projects. The first project involves creating a Long-Short-Term-Memory (LSTM) based Algorithmic Stock Trader, utilizing the sp500 stock market tickers dataset. The implementation includes modeling time series with LSTM, experimenting with techniques like normalization and feature engineering, and assessing the algorithmic trading module’s profitability under various conditions such as buy-ask spread and commissions. The second project focuses on Facial Similarity Metric Learning and Face Generation using Deep Convolutional Generative Adversarial Networks (DCGAN) with the Labeled Faces in the Wild dataset. This project employs a Transfer Learned ResNet based Siamese Network for Similarity Metric Learning, along with experiments involving regularization, learning rate scheduling, dropout, and optimization variations. The DCGAN is trained to generate new faces and modified into a Conditional GAN to generate unseen images based on a given input image from the Siamese Network.