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Neural-Networks-From-Scratch

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

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