Creative Machine Learning with 3 Neural Network Projects

Course Curriculum

Chapter 1: 00a Course Overview

Lecture 1: 01 Project Preview

Lecture 2: 02 Project 2 Preview

Lecture 3: 03 Project 3 Overview

Lecture 4: 04 What You'll Need

Lecture 5: Source Files

Chapter 2: 00b Mammoth Interactive Course Intro

Lecture 1: 00 About Mammoth Interactive

Lecture 2: 01 How To Learn Online Effectively

Lecture 3: Source Files

Lecture 4: 00. Intro To Course And Python

Lecture 5: 01. Variables

Lecture 6: 02. Type Conversion Examples

Lecture 7: 03. Operators

Lecture 8: 04. Collections

Lecture 9: 05. List Examples

Lecture 10: 06. Tuples Examples

Lecture 11: 07. Dictionaries Examples

Lecture 12: 08. Ranges Examples

Lecture 13: 09. Conditionals

Lecture 14: 10. If Statement Examples

Lecture 15: 11. Loops

Lecture 16: 12. Functions

Lecture 17: 13. Parameters And Return Values Examples

Lecture 18: 14. Classes And Objects

Lecture 19: 15. Inheritance Examples

Lecture 20: 16. Static Members Examples

Lecture 21: 17. Summary And Outro

Chapter 3: 01 Machine Learning Fundamentals

Lecture 1: 01 What Is Machine Learning

Lecture 2: 02 What Is Deep Learning

Lecture 3: 03 What Is A Neural Network

Lecture 4: 04 What Is Unsupervised Learning

Lecture 5: 05 Build Models On The Web

Lecture 6: Source Files

Chapter 4: 02 Collect and Process Data

Lecture 1: 01 Load Drawings Dataset

Lecture 2: 02 Label Data

Lecture 3: 03 Build A Training Dataset

Lecture 4: 04 Visualize Dataset

Lecture 5: 05 Batch And Shuffle Data

Lecture 6: Source Files

Chapter 5: 03 Build a Generative Neural Network

Lecture 1: 01 Build A Generator

Lecture 2: 02 Generate Noise

Lecture 3: Source Files

Chapter 6: 03a Generative Neural Network Fundamentals

Lecture 1: 01 What Is A Generative Neural Network

Lecture 2: 02 What Is A Convolutional Neural Network

Lecture 3: 03 How To Build A Convolutional Neural Network

Lecture 4: 04 How To Build A Dense Layer

Lecture 5: 05 How To Build A Batch Normalization Layer

Lecture 6: 06 Leaky Relu Activation Function

Lecture 7: 07 Transposed Convolution Layer

Lecture 8: 08 Hyperbolic Tangent (Tanh) Activation Function

Lecture 9: Source Files

Chapter 7: 04 Build a Discriminator Neural Network

Lecture 1: 00 How Do You Build A Discriminator

Lecture 2: 01 Build A Discriminator

Lecture 3: Source Files

Chapter 8: 05 Evaluate the Model's Performance

Lecture 1: 00 Performance Of A Machine Learning Algorithm

Lecture 2: 01 Calculate Loss

Lecture 3: 02 Assign Optimizers

Lecture 4: 02A What Is The Adam Optimizer

Lecture 5: Source Files

Chapter 9: 06 Train the Model to Draw

Lecture 1: 01 Build A Training Step

Lecture 2: 02 Train The Model

Lecture 3: 03 Visualize Training

Lecture 4: Source Files

Chapter 10: 07 Test the Model's Drawing Ability

Lecture 1: 01 Test The Model

Lecture 2: Source Files

Chapter 11: 08 Build an Image Style Transfer Project

Lecture 1: 00 Style Transfer Project Overview

Lecture 2: 01 Load The Model

Lecture 3: 02 Load Images

Lecture 4: 03 Reformat Image For Machine Learning

Lecture 5: 04 Load Original And Style Images

Lecture 6: 05 Display Processed Images

Lecture 7: 06 Extract Image Features

Lecture 8: 07 Calculate The Style Representation

Lecture 9: 08 Optimize The Model

Lecture 10: 09 Use Machine Learning To Transfer Image Style

Lecture 11: Source Files

Chapter 12: 09 Build an Image Approximation Project

Lecture 1: 01 Load And Process Image

Lecture 2: 02 Build A Training Dataset

Lecture 3: 03 Visualize Training Dataset

Lecture 4: 04 Build A Testing Dataset

Lecture 5: 05 Build A Neural Network

Lecture 6: 06 Train The Neural Network

Lecture 7: 07 Visualize Image Approximation Results

Lecture 8: Source Files