Machine Learning: Neural Networks and Deep Learning · Introduction

Introduction

What this guide covers

A rigorous and formal breakdown of the mathematical, statistical and computational foundations that underpin artificial neural networks and Deep Learning. From the Rosenblatt perceptron to complex attention architectures.

Overview

Topics covered

Introduction
Introduction to Neural Networks and Deep Learning
Origins of Neural Computing
1.1 The McCulloch-Pitts Neuron
1.2 The Rosenblatt Perceptron
1.3 The Perceptron Convergence Theorem

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

y = \Theta \left( \sum_{i=1}^{n} w_i x_i - \theta \right)

View LaTeX source

y = \Theta \left( \sum_{i=1}^{n} w_i x_i - \theta \right)