Repository contains my Jupyter Notebook files (ran either in VSCode using the Jupyter Notebook extension, either Notebook or Lab through Anaconda, or Google Colab) for a Recurrent Neural Network (RNN) regressor model that predicts energy demand in t-horizon, for EEL6812 - Advanced Topics in Neural Networks (Deep Learning with Python) course, PRJ03
Deep Learning Machine Learning Templates
This repository contains code for implementing both Large Language Models (LLM) and Long Short-Term Memory (LSTM) models in AWS SageMaker Studio Lab. It includes notebooks for LLM-based applications and LSTM models for stock price prediction.
This repository features notebooks and datasets for predicting Tesla (TSLA) stock prices using LSTM models. Explore historical data, forecast trends, and gain insights into TSLA's market movements.
In this notebook, I implemented a recurrent neural network (Long short-term memory) using PyTorch that performs sentiment analysis.
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