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

4.9
stars
63,496 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

NC

Aug 19, 2017

Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.

AA

Oct 23, 2017

Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course

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2976 - 3000 of 7,284 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Shashank Y

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Aug 27, 2019

great course, refreshed the knowkedge, thanks to andrew and team

By Faline F

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Aug 25, 2019

The explanation of intuition behind each algorithm is very good.

By Hossein E Z

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Jul 19, 2019

This course was so useful to my knowledge on improving networks.

By Raja s v

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Jun 20, 2019

Great insight, practical approach. Really helpful course Thanks.

By YC X

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Jun 10, 2019

Easy to understand. The professor is very professional and nice.

By Veera V S S P

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May 13, 2019

A simple explanation of the basic concepts. Thank you Andrew Ng.

By Maciej F

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Apr 27, 2019

5 stars for the horse picture in the saddle point explanation :D

By Narayanan S

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Mar 19, 2019

Very practical content. Good introduction to frameworks as well.

By Tushar K

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Feb 2, 2019

This course helped me a lot in understanding the hyperparameters

By Sijie

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Jan 10, 2019

get a lot of insight about how to improve my own neural network.

By Nicolò R

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Sep 27, 2018

Wonderful course, it has both theory and implementation details.

By Stuart C

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Sep 15, 2018

great course which helps with more in-depth neural network ideas

By Krzysztof O

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Sep 2, 2018

Thank you Andrew for this magnificent course and specialization.

By Mohamed C S

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Jul 16, 2018

Excellent course, very clear videos and programming assignments!

By Shobhit G

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Jun 27, 2018

well taught course. The way its taught makes the concepts stick.

By Ed H

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Jun 11, 2018

Very good courses for the learning of DNN optimization problems.

By XIAO N (

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Apr 25, 2018

Very practical advice on how to fine tuning your neural networks

By Rodolfo C

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Mar 22, 2018

Great introduction to hyperparameters adjustment and Tensor flow

By Chertushkin M

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Mar 17, 2018

Fantastic course. Thank you, Andrew NG and deeplearning.ai team!

By S N

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Feb 13, 2018

Great Course with good fundamentals.More exercises would be nice

By Rafael L

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Feb 2, 2018

A bit less interesting than first one, but still a great course.

By Junheng Z

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Jan 14, 2018

It is really great and it help you to get starting to Tensorflow

By Daniel S

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Nov 7, 2017

Great tips for tuning networks and finally know what softmax is!

By Zwiebel

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Nov 7, 2017

Introduction of Tensorflow is concise and to the point as usual!