Project Title:Neural Network from Scratch Using Python

Objective:

To implement a basic neural network from scratch (without using machine learning libraries like TensorFlow or PyTorch) in order to deeply understand how neural networks work internally.

Summary:

This project involves building a simple neural network from the ground up using only Python and NumPy. The goal is to understand the inner workings of neural networks—such as forward propagation, backpropagation, weight updates, and activation functions—without relying on high-level ML frameworks.

Students will manually implement all components, including layers, loss functions, and gradient descent. The neural network can then be trained on simple datasets like MNIST (handwritten digits) or Iris (flower classification) to test its performance.

This project strengthens core machine learning knowledge and math foundations, especially linear algebra, calculus, and optimization techniques.

Key Steps:

Set Up Dataset – Use a simple dataset like MNIST or Iris.

Implement Forward Propagation – Compute outputs layer by layer.

Implement Backpropagation – Calculate gradients and update weights.

Train and Test Model – Evaluate accuracy and visualize learning curve.

Technologies Used:

Python

NumPy (for matrix operations)

Matplotlib (for plotting)

Scikit-learn (optional, for datasets only)

Applications:

Understanding how neural networks learn

Educational tools and tutorials

Foundation for building custom ML models

Entry point to deep learning research

Expected Outcomes:

A fully functional neural network built without ML libraries

Ability to classify data from small datasets

Clear understanding of backpropagation, loss functions, and weight updates