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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
40,838개의 평가
4,347개의 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

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

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

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,283개 리뷰 중 4176~4200

교육 기관: Asad A

Aug 17, 2019

Great videos but wish there were more per-lesson exercises that were there in Course#1 for this track. Also, the transition to TensorFlow was quite abrupt as the key concepts that TF uses are completely new and don't easily borrow from the much cleaner Numpy concepts

교육 기관: Aakarapu S P

Jul 03, 2018

good

교육 기관: Bernardt D

Jun 26, 2018

There were some typos throughout the course.

교육 기관: Gopala V

Oct 24, 2017

Definitely improved my understanding on the tuning

교육 기관: Vasilis S

Sep 26, 2018

Very informative course. The assignments are too trivial. Could've been more challenging.

교육 기관: Venkatraman

Mar 10, 2018

Quite not challenging in the programming assignments

교육 기관: Carlos P

Feb 11, 2018

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

교육 기관: Lian L

Nov 08, 2018

Great introduction to the tuning options for Neural Networks. Would have loved more visual representations of how different options affects learning and accuracy.

교육 기관: Vishnupriya V

Jun 22, 2019

As always Andrew Ng's clearly explains all the concepts along with practical programs. I would strongly recommend doing this course for a good solid understanding of neural networks.

교육 기관: Isaraparb L

Jul 15, 2018

Some of the math may be hard to grasp, but the course gives a lot of useful information overall.

교육 기관: Jeroen V

Nov 14, 2018

The graded functions could be a bit more free form, forcing you to think more about it. I sometimes feel that I'm more solving the "template", than I am thinking about neural nets.

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: Omkar K

Dec 13, 2019

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

교육 기관: Rekhawar N N

Dec 14, 2019

improving Deep Neural networks :Hyperparameter tuning,Regularization and optimization course was amazing! thank you so much coursera and Andrew Ng sir! :))

교육 기관: 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.

교육 기관: Morisetty V A S K

Jan 20, 2019

Interface for evaluating is not great and assignments are easy

교육 기관: Amit C

Feb 01, 2019

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

교육 기관: 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

교육 기관: zhesihuang

Mar 03, 2019

good

교육 기관: 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.

교육 기관: 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.

교육 기관: Ilkhom

Mar 21, 2019

awful sound

교육 기관: 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.

교육 기관: 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

교육 기관: 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.