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Neural-Compression-with-Autoencoders

Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.

  • Updated Jun 1, 2024
  • Jupyter Notebook

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

  • Updated May 10, 2024
  • Jupyter Notebook

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