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

4.9
stars
62,825 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 30, 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.

JS

Apr 4, 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

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6751 - 6775 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Abhishek B

Aug 11, 2020

Goes bit into nity grity, which would be required in the future.

By Keith H

Jun 14, 2020

Always excellent. I wish I had had Andrew as a college prfessor.

By mat s

Mar 8, 2020

the material was good but the assignments could use improvements

By Omkar K

Dec 13, 2019

Really good insight into the inner workings of a neural network.

By Alexander K

Oct 12, 2019

Too less coding and practice exercises, thou the theory is great

By Efthimios K

Jun 13, 2019

Good but need letter recognition NN to understand what he writes

By Emanuel G

Nov 8, 2018

Tensorflow part was quite messy, but besides that, very helpful!

By K B

May 22, 2018

日本語訳があまりなかったので、英語がそこまで得意ではない初心者の人は勉強の順番の工夫が必要だと思う(自分はそれで乗り切りました)

By Akshat J

Jan 13, 2023

Good course, more research paper implementation can be included

By 张一帆

Jan 4, 2022

more code practice maybe is more better to master the knowledge

By Satyaki R

Sep 9, 2021

very good introduction to hyper parameter tuning and Tensorflow

By Anurag P

Apr 25, 2021

Nice lecture and basic definition and function in Deep learning

By Misael D C

May 22, 2020

I had some issues regarding coding, but other than that, great!

By Ananthan J

May 11, 2020

Need further explanation on the optimizer with gradient descent

By Miao Z

Apr 6, 2019

Great course, lecture is perfect. assignments could be improved

By wzh

Jan 10, 2019

代码给的答案和自己写代码运行得到的答案不一样,让我想破脑袋想了很久都不知道哪里错了,结果一提交答案评分的时候又说我是对的,头疼

By Rory W

May 28, 2018

Good overview of optimization methods, but moves a little slow.

By Manuel Á S Á

Sep 24, 2021

I felt that the programming assignments were a little too easy

By Shiv V P

Jul 4, 2020

4 stars due to tensorflow 1 assignment instead of tensorflow 2

By Ryan C

May 8, 2020

A little too much hand holding in the programming assignments.

By vaibhav g

May 5, 2020

Tenserflow section could have been a little bit more elaborate

By Nicolas L

Jan 25, 2020

programming assignment should be more open, with less guidance

By Anway A

Aug 3, 2022

Brilliantly laid out. Simple and challenging at the same time

By Mikhail G

Apr 15, 2020

Very quickly jump to almost profffi TF. It's little suddenly.

By Thomas J D

Nov 8, 2018

Little less well structured/organized than the first course..