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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization(으)로 돌아가기

deeplearning.ai의 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 학습자 리뷰 및 피드백

42,485개의 평가
4,534개의 리뷰

강좌 소개

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization....

최상위 리뷰


Dec 06, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse


Jan 14, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,466개 리뷰 중 4201~4225

교육 기관: Alberto S

May 20, 2018

By itself, not really a couse. It should be part of the first one.

교육 기관: Muhammad W

May 12, 2018

few mistakes in course assignment but overall good course material

교육 기관: Michael F

Apr 20, 2018

The programming assignments were too easy, otherwise good content.

교육 기관: Siyu Z

Mar 19, 2018

A good course. I get familiar with the idea about hyperparameter.

교육 기관: Carlos P

Feb 11, 2018

I would have liked to have more practice exercises about tunning.

교육 기관: Yide Z

Dec 13, 2017

good course but there are some small bugs in video and exercises.

교육 기관: Omkar K

Dec 13, 2019

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

교육 기관: Alexander K

Oct 12, 2019

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

교육 기관: Efthimios K

Jun 13, 2019

Good but need letter recognition NN to understand what he writes

교육 기관: Emanuel G

Nov 08, 2018

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

교육 기관: Yuki K

May 22, 2018


교육 기관: Junyu Z

Apr 06, 2019

Great course, lecture is perfect. assignments could be improved

교육 기관: wzh

Jan 10, 2019


교육 기관: Rory W

May 28, 2018

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

교육 기관: Nicolas L

Jan 25, 2020

programming assignment should be more open, with less guidance

교육 기관: Thomas J D

Nov 08, 2018

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

교육 기관: Qu S

Oct 27, 2018


교육 기관: Anirudh

Jun 28, 2018

not very happy about tensor flow introduction. rest was great

교육 기관: Serdar K

Feb 01, 2018

This was helpful. I advise spending more time on tensorflow.


Oct 23, 2019

Good but need to improve number of examples about tensorflow

교육 기관: Mark

Oct 11, 2018

Good course but a bit more detailed explanations were needed


Sep 28, 2017

good but would have been great if tensorflow is covered more

교육 기관: Henry V

Sep 25, 2017

A very good introduction, but a bit basic for professionals.

교육 기관: Ernst H

Jul 07, 2019

Obvious problems. Lessons and quizzes need to be polished.

교육 기관: Nick R

Jan 07, 2018

Necessary background information and how-to for algorithms.