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Neural Networks Fundamentals: Neurons and Layers

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Overview

This course introduces the core building blocks of neural networks. You'll learn what a neuron is, how it processes information, the role of activation functions, and how neurons are organized into layers. By the end, you'll implement a single dense layer from scratch using JavaScript and array operations.

Syllabus

Unit 1: Neural Networks Fundamentals: Introduction to Neurons and Layers

Neural Network Fundamentals Quiz

Unit 2: Building an Artificial Neuron from Scratch in JavaScript

Initialize a Neuron with Weights and Bias
Implement Input Validation for Neural Network Processing
Implementing the Forward Method for a Neuron
Using a Neuron to Process Data

Unit 3: Introducing Activation Functions: Adding Non-Linearity with the Sigmoid Function

Implementing the Sigmoid Activation Function in a Neuron
Fix Neural Network Forward Pass Order of Operations
Implementing the Sigmoid Activation Function

Unit 4: From Neurons to Layers: Implementing a Dense Layer in JavaScript

Scale Neural Network Weights for Better Training
Fix Neural Network Dense Layer Bias Initialization
Initialize Weight Matrix and Bias Vector for Neural Network Dense Layer
Creating and Inspecting Neural Network Dense Layers
Counting Parameters in a Neural Network Dense Layer

Unit 5: Implementing Forward Propagation in a Dense Layer with JavaScript

Implementing Bias Addition in Neural Network Dense Layer
Fix Matrix Multiplication in Neural Network Forward Propagation
Apply Sigmoid Activation Function to Complete Neural Network Forward Pass
Implementing a Neural Network Forward Pass in JavaScript
Using a Dense Neural Layer with Real Data

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