Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

$43/mo.
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Course Information

Platform iconPlatform: Coursera
Level iconDifficulty: Beginner
Time icon26-40h hours of content
Speed iconStarts Jul 30
Certificate iconCertificate: Certificate (q2)
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Andrew Ng

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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

This course is created or produced by deeplearning.ai via Coursera

Description of the course

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 of "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization"

Practical aspects of Deep Learning



Optimization algorithms



Hyperparameter tuning, Batch Normalization and Programming Frameworks







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