Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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

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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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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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