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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

4.9
40,776개의 평가
4,340개의 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

HD

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

AM

Oct 09, 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

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,275개 리뷰 중 3926~3950

교육 기관: Alejandro E

Feb 19, 2018

Very good course, although it'd be awesome if Andrew went over the backprop associated with Batch Normalization and perhaps a programming example of using Batch norm on my test set.

교육 기관: Mark M

Oct 30, 2017

The intro of hyper parameters was from mathematical point of view as good as the basics of week 1, however practical relevance becomes not really clear.

교육 기관: JaesungHuh

Aug 20, 2017

Good course

교육 기관: Davy

Oct 02, 2017

Interesting, but the quality of the exercises in not so good. There are at least 3-4 mistakes in the expected output that make you loose time double verifying. Mentor only seems to reply it is know, sounding like it has been like this for long...

교육 기관: Paulo A V

Nov 16, 2017

Nice complement to the first course on ANN

교육 기관: Aakarapu S P

Jul 03, 2018

good

교육 기관: Nguyen X M

Sep 07, 2017

The course help me to understand more clearly the optimizers as well as the process of

pyperparameter tuning. I think the assignments should be a little bit more challenging.

교육 기관: Richard J B

Oct 29, 2017

This course had more of a getting-into-the-weeds feel to it, without as much of the broader conceptualizations that the first course had. I also submitted several queries in the forums without getting feedback. Still good, but I hope the following courses are better.

교육 기관: jyning

Dec 03, 2017

感觉作业设计的很好,可以不需要太好的编程能力就能完成,还能加深多算法的理解

교육 기관: Race P V

Nov 27, 2017

I am slow on the uptake on the maths side of the equation, while the repetition of the class lectures is most appreciated. No, it is not repetitive, Andrew keeps expanding on our prior knowledge for each week.

Even with 30 plus years since I did Calculus I am able to follow and understand thanks to the team.

Though, they do need help with correcting some minor mistakes in the webpages.

교육 기관: Mark B

Mar 10, 2018

The material is consistently good, but this is now my third class and I am acclimated to it and self-selecting. Still, I'm not offering 5 stars because this is a Coursera AI course, and I am hypersensitive to the less-than-intelligent interactions that I have with the courseware: I was warned last Sunday about falling behind on my coursework, for example, for a week that I started in the morning but which happened to end that evening. So, the machine-based interaction isn't that great. The discussion lists and mentor interactions are quite good and very helpful. Still, I'm puzzled by the slowness in fixing typos in assignments, these waste our time; I have found at least three of them, all previously documented. So, I am spending time hunting down course errors when I should be hunting down my own.

교육 기관: Jie Y

Mar 10, 2018

The class should include more introduction on the current ml frameworks such as tensor flow etc. Possibly it should include one more project for the ml framework. Hope to give students more experience on the ml frameworks.

교육 기관: Sam M

Apr 28, 2018

Some errors in jupyter notebooks

교육 기관: Mark H

Mar 10, 2018

Could be Greatly improved by having us build a NN using previous learning's with the only change being use of SoftMax for Cost. Then have us use TF to do the same and compare the code effort, and the results 1-to-1...

교육 기관: Guoqin M

Jun 29, 2018

Content is great! A good introduction to a lot of hyper-parameters in neural net. However, there are some bugs in the evaluation system of programming assignments. For example, the system does not recognize Pythons '-=' operation and gave me a fail, which I did not figure out until I saw the forum where people were having the same trouble.

교육 기관: moonseok s

May 28, 2018

very good lecure.

somewhat difficult to me. I will repeat again and again.

교육 기관: Mohammad M R

Jan 03, 2018

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

교육 기관: HS

Dec 16, 2017

Not like the first course which was kind of "trying not to touch the details", this course is more organized and I felt I've learned something. Still I would improve TF training to get more into the details (what does reset global variables do?!)

교육 기관: Anton D

Oct 24, 2017

The course content was of very high quality. There were just some issues in the notebooks that are already covered in the forums. I think it's worth fixing them. In the videos there are also some small mistakes made but nothing serious. Also, about the programming assignment, I think it would be useful to have some in which less of the code is readily written and more is left to the student.

교육 기관: Guy K

Sep 22, 2017

Well organized !! clear explanations !

교육 기관: Joseph A D

Jan 13, 2018

Great course. Thanks for making it available.

I would have enjoyed more tensorflow lectures to help understand the underlying mechanism of the platform. I suppose the intention is to provide that understanding through the assignment, but more discussion in the lecture would be nice.

교육 기관: Kumar V

Oct 16, 2017

Good course could have been better expected little more on Tensor flow exercise.

교육 기관: Alberto S

May 20, 2018

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

교육 기관: Idan P

May 23, 2018

Add more theory

교육 기관: Jayanthi A

Apr 05, 2018

It was great course, however, I would have liked it to be a lot slower with more time being spent on Tensorflow.