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Neural Network from Scratch

Overview

This project involves implementing a neural network from scratch, providing a deep understanding of the underlying mechanics of neural networks without relying on high-level libraries. This hands-on approach is essential for grasping the fundamentals of deep learning.

Features

  • Data Preprocessing: Techniques to prepare data for training.
  • Network Architecture: Custom implementation of neural network layers.
  • Forward Propagation: Calculation of neuron activations.
  • Backward Propagation: Gradient calculation for weight updates.
  • Optimization: Implementation of gradient descent.
  • Evaluation: Assessing the performance of the neural network.

Results

The notebook includes visualizations and performance metrics to help you understand the network's behavior and effectiveness.

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