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Deep Learning Using Keras through a Real-World Case Study
Intro and Essentials
Meet Your Instructor (1:37)
Reference: All Materials
Basic Pipeline for Machine Learning (13:10)
Get Help Here (0:15)
How is Machine Learning Used to Solve New Problems (7:01)
Some Necessary Background
Theoretical Aspects of ML in One Small Video (9:13)
Using Statistics to Learn How Machines Learn (13:01)
Making Yes/No Decisions and Why They Are Important (14:29)
More Information (2:43)
Keras Setup and Intro
Windows Setup and How Not To Have Headaches (6:55)
Linux and Mac Setup (3:41)
Basics of Keras - Getting Ready to Run (10:10)
Learning and Making Predictions with Keras (10:32)
Case Study from Bioinformatics
Real World Bioinformatics Case Study Motivation (8:32)
Getting Data Into a Workable Shape - One of the Most Difficult Tasks in ML (15:51)
Data Wrangling - How to Get it Right (7:45)
Making Sure Machines Cannot Memorize Data (3:11)
The Real Problem of Shapes - Getting your Head Around it (4:32)
Basic Keras Way of Defining Models (8:58)
Detailed and Advanced Model Defining Techniques with Keras (5:25)
CNNs and Advanced Graph Based Models
Convolutional Neural Networks (10:13)
Getting Started with CNN Code - The Easy Way (8:30)
Achieving Translation Invariance (4:25)
Achieve Regulzation With Ease and Efficiency - Dropout (3:51)
Advanced Graph-based Models with Keras (4:27)
Google's Inception Module in Depth (9:36)
Getting Rid of Vanishing Gradients - Residual Connections (5:08)
Extra Material
Progress Saving for Future Reuse (6:30)
Where to Go From Here (3:55)
Getting Rid of Vanishing Gradients - Residual Connections
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