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reccurent-neural-network

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In this model ,I have used the GLUE dataset in order to perform a sentiment analysis on the reviews as positive and negative. Different types of Neural Networks using TensorFlow was tried out such as CNN,GRU and bi-directional LSTMs. The accuracy of predictions of each of these models are visualised in the end

  • Updated Jan 9, 2025
  • Jupyter Notebook

A simple deep learning repository covering ANN, CNN, Transfer Learning, and Sequence Models (RNN, LSTM, GRU) with easy explanations of core concepts.

  • Updated Oct 7, 2025
  • Jupyter Notebook

This project classifies stocks based on tick-by-tick order-book data, as part of the CFM challenge. The model uses GRU layers to process sequential market data and leverages feature engineering on prices, bid/ask sizes, and order types. It’s trained with cross-entropy loss using the Adam optimizer.

  • Updated Oct 28, 2024
  • Jupyter Notebook

This project involved analyzing Amazon user reviews to determine the sentiment expressed (positive, negative, or neutral) using Recurrent Neural Networks (RNNs). The project leveraged deep learning techniques for text classification, processing and transforming the raw text data into a format suitable for RNN input.

  • Updated Mar 8, 2025
  • Jupyter Notebook

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