Neural Networks

Overview

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.

Syllabus

• Unit 1: Building a Perceptron Model
Building a Perceptron for AND Logic Gate Simulation
Debugging Perceptron Summation Calculation
Perceptron Activation Logic Simulation
• Unit 2: Activation Functions in C++
Implementing the Step Function in C++
Visualizing Sigmoid and Tanh Functions
Visualizing ReLU and Softplus Functions
• Unit 3: Backpropagation in Neural Networks
Neural Network Training for XOR Problem
Neural Network Prediction Implementation
Neural Network Weight Update Implementation
Neural Network Enhancement Task

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Subjects

Artificial Intelligence