Learn Introduction to Neural Networks

Description

In this course, you will learn :

• Artificial Intelligence
• Classification
• Backpropagation
• Logic Gates
• Convolutional Networks
• Gradient Descent
• Computer Vision
• Activation Functions
• Universal Approximation

Syllabus :

1. Introduction

• Can Computers Learn?
• The Computer Vision Problem
• The Folly of Computer Programming
• Neural Networks

 

2. Neurons

• The Decision Box
• Binary Neurons
• Decision Boundaries
• Building an XOR Gate
• Classification
• Sigmoid Neurons
• Training a Single Neuron

 

3. Layers

• Hidden Layers
• Curve Fitting
• Universal Approximator
• A Shape-Recognizing Network 

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