Neural Networks Learning Series

A hands-on journey through neural network implementation using Python and NumPy.

Chapters

Chapter 1: Neural Network Fundamentals

Single neuron to multi-layer implementation with manual calculations and programmatic solutions.

• Key formula: output = Σ(input_i × weight_i) + bias
• Manual vs programmatic implementations
• Prerequisites: Basic Python, elementary linear algebra

Chapter 2: Neural Network Layers in NumPy

NumPy implementation progressing from single neuron to batch processing.

• Single neuron, multi-neuron layers, batch processing
• Matrix operations: Y = XW^T + b
• Matrix multiplication, transpose operations, broadcasting

Chapter 3: Multi-Layer Forward Pass

3-layer neural network forward pass implementation.

• Architecture: Input(4) → Hidden1(3) → Hidden2(3) → Output(2)
• Direct matrix operations and loop-based approaches
• Batch processing of 7 samples with 4 features

Chapter 4: Dataset Generator & Dense Layer

Dataset creation and basic layer construction.

• Spiral dataset generation (single/multi-class)
• Dense layer implementation with forward pass
• Integration examples using synthetic data

Chapter 5: Activation Functions & NumPy Operations

Core activation functions and NumPy array operations.

• Activation functions: ReLU, Leaky ReLU, Softmax, Tanh, Sigmoid
• NumPy axis operations and broadcasting concepts
• 2-layer network example with spiral data

Prerequisites

• Python 3.x
• NumPy, matplotlib
• Basic understanding of lists, loops, and linear algebra

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Progressive learning from mathematical foundations to practical neural network implementation.