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This project focuses on predicting future stock prices using recurrent neural networks (RNNs) implemented in TensorFlow. The RNN models are built using basic, LSTM, or GRU cells to capture temporal dependencies and make accurate predictions of stock prices.
In this project, I embarked on a captivating exploration of the stock market, seeking to understand and predict the future performance of three influential companies—Amazon, Zoom, and Walmart
Generate text related to corruption using a Recurrent Neural Network (RNN) based on the Corruption Perceptions Index (CPI) timeseries data from 2012 to 2021.
My study notes and code implementations of Natural Language Processing (NLP) models. Projects include text classification, neural machine translation, Q&A, poetry generation, etc.