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

4.9
stars
63,175 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

AM

Oct 8, 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

CM

Dec 23, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow

Thanks.

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2951 - 2975 of 7,254 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Rangu G

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Apr 1, 2018

Quick recipes for good recap of the content. Thank you Andrew Ng.

By Ravi J

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Mar 1, 2018

It was great exploring various ways to improve my neural networks

By Ahmed A E

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Oct 31, 2017

Great insights and great intro to TensorFlow, really got me going

By cai x

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Aug 31, 2017

Another great course from Andrew. Very practical for beginners!

By nandita s

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Mar 23, 2022

Detailed and elaborate for easy understanding for tuning models.

By Mrinanka D

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Oct 12, 2021

very informative, but could have emphasized on tensor flow more

By Tianyu Z

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Oct 26, 2020

So good! Thank Andrew Ng, you are the person who change my life!

By Mitun K P

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Oct 13, 2020

Great course for better Understanding Regularization Parameters.

By Aaron C

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Jul 28, 2020

Awesome overview, so much to learn, and very carefully presented

By Dinku

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Jul 4, 2020

Excellent explanation of theory, assignments are really helpful.

By Samreen P

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Jun 11, 2020

the teaching is really precise and amazing very helpful thankyou

By Hugues D S

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Mar 22, 2020

Great insight on "rare" local optima in high dimensional spaces.

By Anshul K

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Feb 24, 2020

Gave good insights on the practical aspects of machine learning.

By Roland G

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Oct 3, 2019

As in part 1, good pace, excellent and comprehensive assignments

By Shashank Y

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Aug 26, 2019

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

By Faline F

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Aug 24, 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 19, 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 26, 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 ahmadreza a

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Nov 27, 2018

please correct the mistake in the week 3 assignment, placeholder