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Creative Machine Learning - Draw and Paint with 3 Neural Network Projects
00a Course Overview
01 Project Preview (1:47)
02 Project 2 Preview (1:06)
03 Project 3 Overview (0:47)
04 What You'll Need (2:42)
Source Files
00b Mammoth Interactive Course Intro
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
00c Introduction to Python (Prerequisite)
00. Intro To Course And Python (9:57)
01. Variables (19:19)
02. Type Conversion Examples (10:06)
03. Operators (28:54)
04. Collections (8:24)
05. List Examples (19:41)
06. Tuples Examples (8:36)
07. Dictionaries Examples (14:26)
08. Ranges Examples (8:32)
09. Conditionals (6:43)
10. If Statement Examples (21:32)
11. Loops (29:42)
12. Functions (17:01)
13. Parameters And Return Values Examples (13:54)
14. Classes And Objects (34:11)
15. Inheritance Examples (17:29)
16. Static Members Examples (11:05)
17. Summary And Outro (4:08)
01 Machine Learning Fundamentals
01 What Is Machine Learning (5:26)
02 What Is Deep Learning (6:10)
03 What Is A Neural Network (8:08)
04 What Is Unsupervised Learning (14:58)
05 Build Models On The Web (5:08)
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02 Collect and Process Data
01 Load Drawings Dataset (10:03)
02 Label Data (12:17)
03 Build A Training Dataset (8:30)
04 Visualize Dataset (6:19)
05 Batch And Shuffle Data (4:39)
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03 Build a Generative Neural Network
01 Build A Generator (13:46)
02 Generate Noise (5:41)
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03a Generative Neural Network Fundamentals
01 What Is A Generative Neural Network (7:26)
02 What Is A Convolutional Neural Network (7:04)
03 How To Build A Convolutional Neural Network (14:04)
04 How To Build A Dense Layer (2:42)
05 How To Build A Batch Normalization Layer (1:52)
06 Leaky Relu Activation Function (6:04)
07 Transposed Convolution Layer (5:17)
08 Hyperbolic Tangent (Tanh) Activation Function (2:59)
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04 Build a Discriminator Neural Network
00 How Do You Build A Discriminator (10:19)
01 Build A Discriminator (10:52)
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05 Evaluate the Model's Performance
00 Performance Of A Machine Learning Algorithm (4:14)
01 Calculate Loss (7:11)
02 Assign Optimizers (3:02)
02A What Is The Adam Optimizer (6:55)
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06 Train the Model to Draw
01 Build A Training Step (11:03)
02 Train The Model (6:53)
03 Visualize Training (14:35)
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07 Test the Model's Drawing Ability
01 Test The Model (9:22)
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08 Build an Image Style Transfer Project
00 Style Transfer Project Overview (5:36)
01 Load The Model (4:57)
02 Load Images (6:53)
03 Reformat Image For Machine Learning (7:03)
04 Load Original And Style Images (6:27)
05 Display Processed Images (10:58)
06 Extract Image Features (6:58)
07 Calculate The Style Representation (6:01)
08 Optimize The Model (5:27)
09 Use Machine Learning To Transfer Image Style (13:54)
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09 Build an Image Approximation Project
01 Load And Process Image (7:14)
02 Build A Training Dataset (6:49)
03 Visualize Training Dataset (5:35)
04 Build A Testing Dataset (4:04)
05 Build A Neural Network (7:24)
06 Train The Neural Network (4:39)
07 Visualize Image Approximation Results (5:14)
Source Files
09 Use Machine Learning To Transfer Image Style
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