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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 4051~4075

교육 기관: Juan P S

Sep 14, 2017

Hyperparameters are very well explained. The introduction to Tensorflow is one of the best I've seen. I wish there was more explanation around network architecture: how to choose the number of hidden layers and the number of hidden units in each layer.

교육 기관: Manpreet S B

Oct 03, 2018

good course, easy to understand and very nicely explained concepts about the neural networks

교육 기관: Zahid S

Mar 16, 2019

This course was mostly well-designed especially for the first topics, but in my view, the Tensorflow part needs to be extended. It provided a brief understanding of the topics, but I do believe deeper examples might be helpful.

교육 기관: Kasper J

Oct 01, 2017

Overall a very good course. However, I was hoping for more material on programming frameworks.

교육 기관: Arun J

Sep 17, 2017

really loved the course material but would have loved it more if it gave more in depth tutorials on tensorflow

교육 기관: Jorge

Sep 23, 2017

Good course, Hyperparameter tuning, Regularization and Optimization are well explained, and the Tensor Flow lab is very useful too

교육 기관: Ansgar G

Oct 17, 2019

Andrew Ng is great again. Also the assignments are good with very good explanations for each step in the notebooks. The TensorFlow programming assignment at the end could have gone a bit deeper, with more explanations for things that are used in the end like eval. And it had an error as the third parameter of tf.one_hot is not (anymore?) the shape. You have to explicitly pass it as tf.one_hot(indices, depth, shape=shape).

교육 기관: Lenny F

Sep 29, 2019

Would like to have more practice

교육 기관: Steve I

Sep 27, 2019

This is a great overview for those wanting their neural networks to run more effectively and efficiently. Lots of ideas to improve your networks. The documentation and description of Tensorflow for the exercises is inadequate to be able to diagnose errors in the "expected" code without expert assistance. When debugging Tensorflow for these exercises, its almost a Trial and Error exercise instead of using first principles taught in the presentations.

교육 기관: Jörg N

Oct 19, 2019

I liked the course a lot and I really adore the way Andrew Ng teaches the subject. As an improvement suggestion I would extend the course to four weeks to deepen the practice on Hyperparameter tuning as well as the introduction to Tensorflow. The Programming exercises of week 3 were really challenging. First since there were partially misleading statements in the comments (Z before activation) and second because variables were given the same names as tf parameters and partially even function definitions. So you could see things like a = a, b = b in tf function calls which just does not fit for beginners in portentously both Python (local variables concepts, etc.) and TF. I am more than grateful though that I could do this course of the specialisation and I would really like to express my deep gratitude to Andrew Ng.

교육 기관: Mohamed S

Oct 20, 2019

e

교육 기관: David R

Oct 01, 2019

(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

교육 기관: Aurangazeeb A K

Sep 30, 2019

Although I loved this course, I believe there are certain parts that could be broken down into even simpler intuitions. If such a change a possible, this course will be the best one out there. Anyway, I really enjoyed the course and it was a great learning experience. Tensorflow was introduced very finely and it aroused my curiousity to learn more.

교육 기관: Mustafa S Ç

Oct 22, 2019

Everything was great. Every peace of information scratch in my mine. I learned a lots from course.

In the last part; Tensorflow has dramaticly changed but content didn't renewed.

교육 기관: Huy T T

Dec 04, 2019

Overall, it's pretty good. I did have a problem understanding some of the facts being communicated about gamma and beta in batch norm. Also, I think there is a problem with the last notebook. My cost did not go down as fast.

교육 기관: אוריאל ב

Dec 05, 2019

Hi

I enjoy the course a lot!

for tensor flow - I am not sure if its me or the course - but I need much more training to start thinking the tensor flow way. maybe i will practice more on real work cases.

thanks !

Oriel

교육 기관: Emmanuel T

Oct 03, 2019

Compared to previous module, this one was more of a cookbook and I expected more mathematics in terms of why each optimization work.

Overall, it was still a very interesting hands on approach, finishing with TensorFlow is a bit more difficult to apprehend as all the previous exercices were done in a very different way (Numpy).

교육 기관: 侯凌

Dec 02, 2017

Need slides and notes

교육 기관: Ruixin Y

Apr 30, 2018

The course itself is great, but the notebook (programming assignment system) is not stable, it's annoying sometimes.

교육 기관: Junyu Z

Apr 06, 2019

Great course, lecture is perfect. assignments could be improved

교육 기관: 김연희

Dec 11, 2018

좋은 강좌입니다. 단 한글 번역 부분에 오류가 많습니다. 이후에는 수정되었으면 좋겠습니다.

교육 기관: Julian F

Sep 30, 2017

A very practical hands-on study.

교육 기관: Keanu T

Jun 26, 2019

I wish it went a little more in-depth with softmax classifiers but I can find that online so it's good.

교육 기관: Hamza E B

Jun 22, 2019

Great Course ! I learned a lot, but I would have preferred another Framework though (like Pytorch) ...

교육 기관: Mohd F B Z

Aug 29, 2017

Good!