Neural Networks Basics from Scratch

Dive deep into the theory and implementation of Neural Networks. This course will have you implementing tools at the heart of modern AI such as Perceptrons, activation functions, and the crucial components of multi-layer Neural Networks. All of this without the help of high-level libraries leaves you with a profound understanding of the underpinning mechanisms.

Lessons and practices

Lesson 1: Understanding Neural Networks: An Introduction to the Perceptron Algorithm

Perceptron Logic Probe Decision-Making

Perceptron Prediction Calibration

Code the Perceptron's Decision Function

Lesson 2: Understanding and Implementing Neural Network Activation Functions

Step Function in Action

Comparing Function: Sigmoid and Tanh

Sigmoid Activation Function Implementation

Lesson 3: Backpropagation Unveiled: Understanding the Mathematics and Code Behind Neural Network Learning

Training a Neural Network to Solve XOR

Implement Prediction in the Neural Network

Implement Weight Update in Neural Network

Implement Sigmoid Derivative and Prediction Function in a Neural Network

Interested in this course? Learn and practice with Cosmo!

Practice is how you turn knowledge into actual skills.