Implementing Deep Learning Architectures for Advanced Machine Learning using PyTorch
Click for more details about the project
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 S&P 500 stock-market ticker dataset. The implementation includes modeling time series with LSTM, experimenting with techniques such as normalization and feature engineering, and assessing the algorithmic trading module’s profitability under various conditions such as bid-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.