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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 Python and NumPy.

Syllabus

Unit 1: Understanding Neural Networks: The Big Picture and Your Learning Path

Quiz on Artificial Neurons and Networks

Unit 2: The Artificial Neuron: Building the Foundation of Neural Networks

Initializing the Artificial Neuron
Validating Inputs for Your Neuron
Calculating the Neuron Output
Putting the Neuron to Work

Unit 3: Activation Functions: Introducing Non-Linearity with Sigmoid

Activating the Neuron Output
Order Matters in Neural Activation
Build the Sigmoid Function Yourself

Unit 4: Building a Dense Layer

Scaling Weights for Better Layers
Biases That Fit the Layer
Build the Heart of a Layer
Exploring Dense Layer Structure
Counting Parameters in Dense Layers

Unit 5: Forward Propagation through a Layer

Adding Biases in Neural Layers
Fixing the Forward Pass Calculation
Activating Outputs in Neural Layers
Build a Neural Layer from Scratch
Running a Neural Layer Forward Pass

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