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Part 2: Considering Deep Learning Basics
Curriculum
7 Sections
23 Lessons
10 Weeks
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Chapter 5: Reviewing Matrix Math and Optimization
3
1.1
Revealing the Math You Really Need
10 mins
1.2
Understanding Scalar, Vector, and Matrix Operations
10 mins
1.3
Interpreting Learning as Optimization
10 mins
Chapter 6: Laying Linear Regression Foundations
5
2.1
Combining Variables
10 mins
2.2
Mixing Variable Types
10 mins
2.3
Switching to Probabilities
10 mins
2.4
Guessing the Right Features
10 mins
2.5
Learning One Example at a Time
10 mins
Chapter 7: Introducing Neural Networks
3
3.1
Discovering the Incredible Perceptron
10 mins
3.2
Hitting Complexity with Neural Networks
10 mins
3.3
Struggling with Overfitting
10 mins
Chapter 8: Building a Basic Neural Network
2
4.1
Understanding Neural Networks
10 mins
4.2
Looking Under the Hood of Neural Networks
10 mins
Chapter 9: Moving to Deep Learning
5
5.1
Seeing Data Everywhere
10 mins
5.2
Discovering the Benefits of Additional Data
10 mins
5.3
Improving Processing Speed
10 mins
5.4
Explaining Deep Learning Differences from Other Forms of AI
10 mins
5.5
Finding Even Smarter Solutions
10 mins
Chapter 10: Explaining Convolutional Neural Networks
3
6.1
Beginning the CNN Tour with Character Recognition
10 mins
6.2
Explaining How Convolutions Work
10 mins
6.3
Detecting Edges and Shapes from Images
10 mins
Chapter 11: Introducing Recurrent Neural Networks
2
7.1
Introducing Recurrent Networks
10 mins
7.2
Explaining Long Short-Term Memory
10 mins
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