Neural-Networks-From-Scratch

A collection of code written while working through the book: 'Neural Networks from Scratch'.

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Table of Contents

Chapter 1: Introducing Neural Networks

• A Brief History
• What is a Neural Network?

Chapter 2: Coding Our First Neurons

• A Single Neuron
• A Layer of Neurons
• Tensors, Arrays, and Vectors
• Dot Product and Vector Addition
• A Single Neuron with NumPy
• A Layer of Neurons with NumPy
• A Batch of Data
• Matrix Product
• Transposition for the Matrix Product
• A Layer of Neurons & Batch of Data with NumPy

Chapter 3: Adding Layers

• Training Data
• Dense Layer Class

Chapter 4: Activation Functions

• The Step Activation Function
• The Linear Activation Function
• The Sigmoid Activation Function
• The Rectified Linear Activation Function
• Why Use Activation Functions?
• Linear Activation in the Hidden Layers
• ReLU Activation in a Pair of Neurons
• ReLu Activation in the Hidden Layers
• ReLU Activation Function Code
• The Softmax Activation Function