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from-scratch-in-python

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Over several weeks, I manually implemented every stage of a convolutional neural network using only NumPy—without relying on TensorFlow or PyTorch. The network uses Xavier and He initialization, supports both Leaky ReLU and standard ReLU activations, and applies L2 regularization with exponential learning rate decay to improve generalization.

  • Updated Jun 11, 2025
  • Python

This repository explores building a character-level transformer decoder in PyTorch, similar to GPT while focusing more on understanding individual components. My goal is to gain deep transformer knowledge and see if character-level learning improves handling of unseen words. The code allows for hyperparameter tuning and experiment customization.

  • Updated Aug 30, 2024
  • Jupyter Notebook

A minimal NumPy-based implementation of a 3-layer convolutional neural network (CNN) from scratch — including custom forward and backward passes for conv, ReLU, pooling, affine, and softmax layers. Perfect for learning how CNNs actually work under the hood.

  • Updated May 20, 2025
  • Jupyter Notebook

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