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

4.9
stars
63,068 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

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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.

YL

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very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

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3001 - 3025 of 7,239 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By junping z

Sep 29, 2019

very useful course and bring insight on how to train parameters

By Xia H

Aug 5, 2019

Great explanation like always from Prof. Ng. Thank you so much!

By Juha J

Jul 17, 2019

First it was easy but then I really had to start using my brain

By Yuri

Mar 8, 2019

Great course but why there is not downloadable PDF with slides?

By Akash C

Oct 4, 2018

Very nice course with good material and programming assignments

By neeraj c

Apr 12, 2018

Complex concepts made simple by building upon using easy steps.

By Yu S

Feb 11, 2018

I hope instructors could fix the wrong notation in the lecture.

By Abhishek

Dec 29, 2017

Nice course> i loved to be a part of it.Just needs lots of time

By Li Z

Nov 25, 2017

I especially liked many "intuition"s on deep learning concepts.

By Leo S

Oct 18, 2017

Necessary to understand how to use neural networks in practice.

By QIQING

Sep 25, 2017

thanks to Andrew, for his shareing of experience in this field.

By Sebastian F

Sep 15, 2017

Hands on learning! Interesting lessons and easy to follow code.

By Rabiya M

Dec 6, 2023

Extraordinary Course with exceptional learning points. Great!!

By KUSHAGRA S

Dec 16, 2021

Great! Though last assignment as a bit difficult but its okay,

By Maya S

Sep 26, 2020

amazing course ! well build and explained thank you very much!

By Satyam S

Aug 28, 2020

Really helped me build my fundamentals about hyper parameters.

By YANSKY

Jul 28, 2020

This course is excellent! Every material explained very clear.

By Muhammad U

Jul 17, 2020

Really helps you to understand what is going behind the scene.

By Anirban G

Jul 5, 2020

Excellent basic introduction to hyper-parameter optimizations.

By sathya p

Jun 17, 2020

Well done guys. Great work indeed.

Hope to see courses from you

By Harshit S

May 29, 2020

Good way to present the concept and then explain the intuition

By Muhammad F B I

May 17, 2020

this Course is wonderful. I would strongly recommend this one.

By Zhi L

Mar 17, 2020

Thanks to Andrew and the team for providing such great course!

By 靳文彬

Mar 9, 2020

Andrew is a great teacher, the whole series is almost perfect!

By John G

Feb 16, 2020

Great overview of optimizing networks and intro to tensorflow.