Neural Networks from Scratch

Introduction

• Goal: Create neural networks from scratch in Python, without libraries, then using NumPy.
• Purpose: Deeply understand how neural networks work to apply knowledge to frameworks like PyTorch or TensorFlow.

Why Build From Scratch?

• Deep learning can feel pre-solved with predefined layers and functions.
• Custom problem-solving requires deep understanding.
• Neural networks involve many small, simple computations.

Structure of Neural Networks

• Layers: Input, hidden, and output layers.
• Neurons: Nodes in each layer connected by weights and biases.
• Forward Pass: Input data -> hidden layers -> output.
• Example: Predicting failure or non-failure from sensor data.

Understanding Components

• Weights and Biases: Each connection has a unique weight; each neuron has a unique bias.
• Activation Functions: Applied to neuron outputs. Common ones include ReLU and softmax.
• Loss Function: Measures how wrong the network's output is; example is negative log loss.

Forward Pass Calculations

• Basic Operations: Input x Weights, summing, adding bias, activation function.
• Code Example: Simple forward pass implementation in Python.

Requirements

• Programming Knowledge: Basic understanding of Python and Object-Oriented Programming (OOP).
• Python Version: Python 3.7 and NumPy 1.18.2 recommended.
• Editor: Any code editor like Sublime Text.

Practical Guidance

• Treat this as a multi-session learning endeavor.
• Use the "Neural Networks from Scratch" book as a supplementary resource.
• No deep learning background needed, basic programming, and optionally, supplementary math resources like Khan Academy for Linear Algebra and Calculus.

First Coding Steps

• Setting Up: Use Sublime Text or any editor; setup build system for Python.
• Coding a Neuron: Example calculation with made-up inputs, weights, and a bias value.
• Simple Calculation: Implementing forward pass for a single neuron in basic Python.

Additional Resources

• Book: "Neural Networks from Scratch" book available with more details.
• Community Help: Ask for help in comments or join the Discord group.
• Future Content: Continued series of videos breaking down each step.

Final Notes

• Neural Networks are fundamentally a collection of simple computations.
• Aim to break down understanding into manageable steps.
• Keep progressing through small, understandable increments.