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Deep Learning Specialization
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Course 2
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Module 2
Syllabus
Courses
Course Syllabus
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Module 1: Practical Aspects of Deep Learning
Setting up your Machine Learning Application
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Regularizing your Neural Network
Setting Up your Optimization Problem
Lecture Notes (Optional)
Quiz
Programming Assignments
Heroes of Deep Learning (Optional)
Module 2: Optimization Algorithms
Optimization Algorithms
Mini-batch Gradient Descent
Video
・
11m
Understanding Mini-batch Gradient Descent
Video
・
11m
Exponentially Weighted Averages
Video
・
5m
Understanding Exponentially Weighted Averages
Video
・
9m
Bias Correction in Exponentially Weighted Averages
Video
・
4m
Gradient Descent with Momentum
Video
・
9m
RMSprop
Video
・
7m
Clarification about Upcoming Adam Optimization Video
Reading
・
1m
Adam Optimization Algorithm
Video
・
7m
Clarification about Learning Rate Decay Video
Reading
・
1m
Learning Rate Decay
Video
・
6m
The Problem of Local Optima
Video
・
5m
Lecture Notes (Optional)
Quiz
Programming Assignment
Heroes of Deep Learning (Optional)
Module 3: Hyperparameter Tuning, Batch Normalization and Programming Frameworks
Hyperparameter Tuning
Batch Normalization
Multi-class Classification
Introduction to Programming Frameworks
Lecture Notes (Optional)
Quiz
End of access to Lab Notebooks
Programming Assignment
References & Acknowledgments
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Deep Learning Specialization
Intermediate
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Topics
Computer Vision
Deep Learning
NLP
Supervised Learning
Transformers
Collaborator
DeepLearning.AI
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