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Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

 with  Andrew Ng
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.

Syllabus

Practical aspects of Deep Learning


Optimization algorithms


Hyperparameter tuning, Batch Normalization and Programming Frameworks


1 Student
review
Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 3 weeks long
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1 review for Coursera's Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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3 weeks ago
Silveira Homero completed this course, spending 9 hours a week on it and found the course difficulty to be medium.
This is a follow up course to Neural Networks and Deep Learning so you must start with the latter. The practical side of the teaching was very interesting.
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