Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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FREE,
Add a Verified Certificate for $49

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4694 reviews

Course Description

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.
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Reviews 9/10 stars
1 Review for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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Oles Tourko profile image
Oles Tourko profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
1 year, 7 months ago
This course is part of the Deep Learning specialization. Week 1: Regularisation and why/how it works. Week 2: Optimization algorithms. It turns out that gradient descent is not the only implementation you can use! Week 3: Guidelines on how to tune your hyperparameters, batch normalization (a technique to accelerate learning), and an introduction to Tensorflow. Assignments are in Numpy (using Jupyter notebooks), with a small Tensorflow exercise in the end. Overall a great course when coupled with the first course in the Deep Learning specialization.
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