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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Gain insight into a topic and learn the fundamentals.
4.9
(63,219 reviews)
Intermediate level
Recommended experience
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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4.9 (4,857 ratings)Offered by
Recommended if you're interested in Machine Learning
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4.9
63,219 reviews
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Showing 3 of 63219
NC
5
Reviewed on Aug 18, 2017
Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.
YL
4
Reviewed on Feb 20, 2018
very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.
XG
5
Reviewed on Oct 30, 2017
Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.
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