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Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

4.9
stars
62,825 ratings

About the Course

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. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

XG

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.

JS

Apr 4, 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

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5901 - 5925 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Diwakar S

Sep 27, 2020

good

By Pradeep

Sep 19, 2020

good

By Nitesh N

Sep 11, 2020

cool

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Sep 1, 2020

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Aug 24, 2020

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Aug 22, 2020

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Jun 28, 2020

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Jun 16, 2020

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By 17MI556 R L S

Jun 13, 2020

Best

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May 11, 2020

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May 10, 2020

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May 6, 2020

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Apr 21, 2020

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Apr 12, 2020

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Mar 16, 2020

NIce

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Feb 10, 2020

nice

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Feb 8, 2020

good

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Jan 9, 2020

Good

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Jan 6, 2020

Good

By VENKATESAN S

Nov 7, 2019

good

By yinxiaobao

Oct 22, 2019

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