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Back to Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

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

4.9
stars
63,283 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 31, 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.

AS

Apr 19, 2020

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

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6876 - 6900 of 7,270 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Seth T

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Mar 26, 2018

Short course but was really excited to delve into TF.

By 王婷

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Feb 17, 2018

good PA examples, that could benefit my further study

By Arhan G

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Jan 31, 2018

Andrew is a great lecturer. The videos are excellent.

By Alaa B

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Jun 13, 2023

very useful for both academic and business purposes

By Nicolas M

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Jun 20, 2021

a little more practice on TF would have been nice...

By Hak K C

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Apr 5, 2018

Course was concise and assignments were well guided.

By Stuart R

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Mar 11, 2018

Good course. Minor errors/typos in presented videos.

By Venkatraman N

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Mar 10, 2018

Quite not challenging in the programming assignments

By Uday Y

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Apr 30, 2020

Tensorflow assignment should be modified to use 2.x

By Dmitry K

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Apr 14, 2020

TensorFlow should be updated to the latest version.

By Fangshi L

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May 19, 2019

Good course, although some bugs in homework grading

By Sumeet R

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Feb 10, 2019

very good course - gets to practical aspects of ML!

By Juan O

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Dec 3, 2017

Having slides like in other courses will be helpful

By SPS P

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Jun 27, 2020

Tensorflow could have been taught in a better way.

By Sen C

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Dec 24, 2019

There should have been more exercise on tensorflow

By Gopal M

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Sep 15, 2019

TensorFlow is a bit nebulous.I need more practice.

By Jean-Marc S

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Dec 27, 2018

The syntax and logic of tensorflow is a bit blurry

By Daniel F P R

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Dec 17, 2018

Was great! Would have loved to see more tensorflow

By Kim y h

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Dec 11, 2018

좋은 강좌입니다. 단 한글 번역 부분에 오류가 많습니다. 이후에는 수정되었으면 좋겠습니다.

By Shaun M

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Jul 8, 2018

Good follow-on from course 1 of the specialization

By Mohammad M R

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Jan 3, 2018

Sorry for the last review - the quiz can be saved.

By Gopala V

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Oct 24, 2017

Definitely improved my understanding on the tuning

By erhan b

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Oct 21, 2017

Assignments are mostly copy past from instructions

By Agnes

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Oct 13, 2017

it is very useful for the processing of modelling.

By ZHE C

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Aug 18, 2017

include key idea on the tuning of hyperparameters