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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
63,243 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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6876 - 6900 of 7,261 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

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 2, 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 14, 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 20, 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 17, 2017

include key idea on the tuning of hyperparameters

By Ahmed H

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Dec 19, 2022

multiclass classification need more illustration

By pranav b

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Jan 28, 2020

Best Course For Learning Hyper parameters Tuning

By 윤동준

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Mar 7, 2019

REALLY USEFUL.

BUT IT IS BIT HARD FOR BEGINNERS.

By Sumitabha B

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Jun 9, 2022

The last tutorial on Tensorflow could be better

By Shoaib Z

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Dec 29, 2020

1st and 2nd weeks are still somewhat confusing.

By bahri d

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Nov 25, 2020

Teorik anlatım harika ama uygulama kısmı eksik.

By JEROME D

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Sep 16, 2020

More than 10 videos is too much for just 1 quiz