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Analyzing and predicting Google's stock prices through detailed data exploration and advanced LSTM models. This project involves data preprocessing, creating time-series sequences, constructing and training LSTM networks, and evaluating their performance to forecast future stock prices utilizing Python and Machine Learning libraries.

  • Updated Jun 6, 2024
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This project is an interactive Streamlit web application using the VGG-13 model to classify images from the CIFAR-10 dataset. Users can upload images to receive real-time predictions and visual explanations of the model's decisions. The goal is to accurately classify images into one of the ten CIFAR-10 classes: airplanes, automobiles, birds, cats,

  • Updated Jun 6, 2024
  • Jupyter Notebook

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