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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
40,643개의 평가
4,326개의 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

CV

Dec 24, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

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 4,261개 리뷰 중 4176~4200

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: 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

교육 기관: CARLOS G G

Jul 14, 2018

good

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: 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.

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.

교육 기관: HAMM,CHRISTOPHER A

Apr 30, 2018

Lots of theory and not enough practical implementation.

교육 기관: QUINTANA-AMATE, S

Mar 11, 2018

Again, nice videos but not

교육 기관: 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.

교육 기관: Amod J

Mar 18, 2018

Want to download my own work but cannot.

교육 기관: 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.

교육 기관: Tushar B

Jun 12, 2018

Assignments vs lecture, difference is huge

교육 기관: FREDERIC T

May 13, 2018

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

교육 기관: Akhilesh

Mar 14, 2018

enjoyed :)

교육 기관: Juan J D

Sep 11, 2017

tensorflow subject was to superficial

교육 기관: John D G

May 24, 2018

the lectures in this course seemed very packed and rushed, squeezing in a lot of content that felt skipped over instead of delving into the math a bit. The jupyter notebooks also have alot of errata that haven't been updated in a while

교육 기관: Virgilio E

Nov 27, 2017

The course explains great tips for optimizing and tuning NN, bu I miss some more practical examples where observing and compare results when applying the different techniques studied.

Also I miss a general schema of all optimization and tuning tips in order to know when and where apply each depending on conditions, etc.

교육 기관: Andrey L

Oct 01, 2017

week 2 was extremely boring

교육 기관: Laura L

Mar 22, 2018

It does not make you think of the problems, just fill in the gaps. First course was better.

교육 기관: Maisam S W

Oct 04, 2017

I still find tensorflow hard.

교육 기관: Li X

May 12, 2018

Good: Contents on Tensor Flow

Bad: No real useful content compared the Course 1.

교육 기관: Patrick P

Sep 22, 2017

The course notes don't lend themselves for use as reference materials. The programming exercises are spoon-fed. The material is more up-to-date than Andrew Ng's Machine Learning course, but that set a higher standard for online education.

교육 기관: Kenneth Z

Mar 20, 2018

It is a bit abrupt to jump into tensorflow without explaining in depth.