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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization に戻る

deeplearning.ai による Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization の受講者のレビューおよびフィードバック

4.9
40,776件の評価
4,340件のレビュー

コースについて

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....

人気のレビュー

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

フィルター:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization: 4176 - 4200 / 4,270 レビュー

by Noah M

Dec 10, 2019

With the basic knowledge I earned in course 1, it was very helpful attenting this coruse on improving Deep NN and I took a lot of notes during the course, to which can refer in the future.

by Vikash C

Jan 28, 2019

Content was good.

But the system that checks our submitted our code checks wrongly even when I wrote it correctly.

In week 2 assignment, when I submitted the code, it gave many functions as wrong coded.

I resubmitted the code after few changes, for instance a+= 2 changes to a = a+2 and string text like 'W' changes to "W". It worked fine and gave 100 points.

In short, what I observed is that the code checking system is taking a+=2 and a=a+2 as differently, also 'W' and "W" are considered different, but they are not in actual output.

by Morisetty V A S K

Jan 20, 2019

Interface for evaluating is not great and assignments are easy

by Kartheek

Feb 01, 2019

week 3 topics would have been a bit better

by Amit C

Feb 01, 2019

I wish the course mentors were more active on this course makes it a bit difficult to clear doubts

by srinivasa a

Jan 09, 2019

its great foundational course but i feel with frameworks available the math behind it was little boring.Andrew NG is pretty good with explaining it well but sometimes felt it was too trivial

by Long H N

Feb 13, 2019

N/A

by zhesihuang

Mar 03, 2019

good

by Jorge G V

Mar 07, 2019

The lessons are good, the programming assignment has mistakes that have apparently been reported over a year ago and have yet to be fixed - there is no excuse for this to be the case.

by Till R

Mar 02, 2019

Exercises are too easy, and lectures are kind of boring. The Jupyter / iPython system does not run smoothly. I ended up downloading everything on my local computer, completing the assignment there, and then pasting the code into the coursera notebook. That makes the assignments take 50% longer than necessary.

by Ilkhom

Mar 21, 2019

awful sound

by Tan K L

Jan 26, 2019

I think more should be done regarding the TensorFlow framework with more explanations given to what the functions did

by CARLOS G G

Jul 14, 2018

good

by Navaneethan S

Sep 20, 2017

This course was much less rigorous and theoretically-grounded than the first. There didn't seem to be much justification for any of the techniques presented, which was a stark contrast to the first course.

However, the topics are important and useful to know, so I'm glad they were covered. To me, the most useful sections were on softmax regression and deep learning frameworks, which I really enjoyed. The TensorFlow assignment was also interesting and (relative to the others) challenging.

I think there is a lot of scope for this course to be improved and I hope Dr Ng and team will do so in the near future.

by Nikolay B

Dec 05, 2017

Lessons are nicely explained

Assignments should be more challenging. Same as first course, this one basically make you cope-paste instructor notes and just change variable names to pass all assignments.

by Todd J

Aug 18, 2017

Very mixed feelings about this course. The course title and nearly all (but 20 minutes) of the video content are on the topic of hyperparameter tuning, regularization and optimization of neural nets. This material is excellent. However, the programming assignment for Week 3 is about building a simple model in Tensorflow, with no coverage the rest of the material from the week. It is as if they included the wrong assignment, or just forgot to include the appropriate assignments to practice the actual content of the course. In addition, the Tensorflow intro in the videos and the Tensorflow assignment are not that great an introduction to the concepts behind Tensorflow. There are much better tutorials available on the web, such as from Tensorflow.org and codelabs.developers.google.com

by Minglei X

Oct 22, 2017

Some process that was discussed in details in previous courses are mostly omitted in new context. While it is sometimes nice for saving time and focusing on new ideas, I feel like there are sometimes subtleties in them. Like I could not imagine how backward propagation should be implemented in batch norm. I'm not sure if it's because there are really some subtleties that you think it's too tedious and not necessary to introduce in the short video. If it is the case, I still hope you could provide more detailed information about them somewhere, just for curious people like me.

by HAMM,CHRISTOPHER A

Apr 30, 2018

Lots of theory and not enough practical implementation.

by QUINTANA-AMATE, S

Mar 11, 2018

Again, nice videos but not

by Dimitrios G

Nov 28, 2017

The course continues on the same path the previous Deep Learning course has set but I found the use of TensorFlow somewhat limiting. It is a great tool that simplifies the training and running of NNs but it does not allow for easy debugging or for easy looking within the built-in functions to spot problems. I felt that we were treating many tf.functions as black boxes and I am not so fond of this. Otherwise the course was fairly useful.

by Amod J

Mar 18, 2018

Want to download my own work but cannot.

by Younes A

Dec 07, 2017

Wouldn't recommend because of the very low quality of the assignments, but I don't regret taking them because the content is great. Seriously the quality of deeplearning.ai courses is the lowest I have ever seen! Glitches in videos, wrong assignments (both notebooks and MCQs), and no valuable discussions on the forums. Too bad Prof Ng couldn't get a competent team to curate his content for him.

by Tushar B

Jun 12, 2018

Assignments vs lecture, difference is huge

by FREDERIC T

May 13, 2018

Good courses, the sound quality is very poor (high tone noise).

by Akhilesh

Mar 14, 2018

enjoyed :)