In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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Intermediate level
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Flexible schedule
Approx. 23 hours
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Assessments
3 assignments
Taught in English
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This course is part of the Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
What's included
15 videos5 readings1 assignment3 programming assignments
Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models.
What's included
11 videos3 readings1 assignment1 programming assignment
Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.
What's included
11 videos7 readings1 assignment1 programming assignment
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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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