Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization(으)로 돌아가기

4.9

40,643개의 평가

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4,326개의 리뷰

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....

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.

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:

교육 기관: Mukesh K

•Aug 19, 2019

The content of the Course is very precise and assignment truly reflect what is been taught in the lectures. Explanation and presentation of algorithms are what I like the most. Assignment were very engaging and interesting.

교육 기관: Gerrit V

•Aug 19, 2019

Sometimes quit slow

교육 기관: Nguyễn H T

•Aug 20, 2019

I think this course is great. Because we learn about some definitions about hyperparameters, optimization which are frequently appears in papers or in the functions in some Deep Learning frameworks.

교육 기관: Elpidio E G V

•Apr 23, 2019

Great explanations on behind the scenes operations of optimization algorithms and general theory. Coming from a more practical background, it helped me grasp the concepts much better. I only wish the programming exercises were a little bit more challenging!

교육 기관: Surya J

•Apr 23, 2019

Great course to build intuition about tuning NN. Solid Foundation in very short duration.

교육 기관: Marc D

•Sep 14, 2019

The course really takes the student by the hand through the exercises. The disadvantage is that it is not really necessary to understand what you are doing. Just follow the guidance. But on the whole really satisfactory

교육 기관: Khalid A

•Sep 15, 2019

It is definitely very informative, but I wish the lectures would be more in depth in regards to the derivation and proofs.

교육 기관: Gopal M

•Sep 14, 2019

TensorFlow is a bit nebulous.I need more practice.

교육 기관: Cristhian A B

•Aug 28, 2019

It's a hard course but the materials are great and their explanations

교육 기관: Daniel E B G

•Aug 26, 2019

I think this course would benefit from a little more explaining. There are a lot of new concepts and some explanations were too quick in my opinion.

교육 기관: Ralf S

•Aug 28, 2019

Good course overall. but labs could be expanded. Don't know if the Coursera platform supports it, but labs between lectures about different topics would be nice instead of having all practical exercises at the end.

교육 기관: Mor k

•Aug 30, 2019

excellent

교육 기관: Siddhi V T

•Sep 19, 2019

An awesome course for someone who wants to learn how to tune the hyperparameters of their models.

교육 기관: Michael R

•Nov 02, 2019

Tensor flow should be explained in more detail

교육 기관: Marcos C D

•Nov 03, 2019

Content needs update to leverage the state of the art in the subject.

교육 기관: Joao N

•Nov 05, 2019

One again the course is a great follow up from the previous one. The only little detail I wish had been done was for the assignment to cover a scenario where we had to improve some hyperparameters by applying different approaches covered in class.

교육 기관: Gilad F

•Nov 03, 2019

I'd make the tesnsorflow section a separate week with much more elaboration, the first time (in both course 1 and course 2) I felt a subject was lacking information. It's mostly noticeable in the programming assignment.

교육 기관: Fabio S

•Nov 04, 2019

Suggestion of references, as a complement, would be very interesting.

교육 기관: Ramesh K

•Nov 04, 2019

I have taken Machine Learning courses earlier from Andrew Ng via Coursera. I have always felt that the delivery of the material and the pedagogy are superb and have always rated a 5 star as also for the first course in this specialization. This second course had several interesting topics I had never learned in my previous NN courses at universities. The programming exercises for weeks 1 and 2 were excellent in helping recap the material in the videos and slides. However as far as TensorFlow is concerned, I was a bit disappointed because it seemed like we were muddling through the various code snippets rather than getting a firm grasp of what is obviously a very complex Deep Learning programming framework. But I understand the time limitations and I realize that this intro to TensorFlow is merely to whet one's appetite and encourage us to explore more about this framework as well as other frameworks. I believe it is up to each individual to explore the concepts further and get a better understanding.

The technology behind the courses is awesome as well as the programming assignment notebooks which were well documented and must have taken gargantuan amount of time and effort in prepping.

In summary, I learned a lot from this course and while my course objectives were not fulfilled, almost all of them were.

교육 기관: Eamonn G

•Sep 04, 2019

Overall good class.

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: José D

•Sep 07, 2019

This is Course2 of the Deep Learning Specialization. In Course1, we learned how to code the algorithm in Numpy. Most of Course2 show how to optimize and tune the algorithm and how to use and tune the hyper-parameters. Most assignments are well-designed and easy to perform as they focus more on the understanding than "finding how to code it". However, the last assignment introduces TensorFlow where we re-implement the algorithm using TensorFlow concepts. I have to say I expected TensorFlow to simplify things but it turns out I find the Math/Numpy implementation way easier to understand than TensorFlow. I'll have to dig deeper in TensorFlow concepts to understand it better. I would have liked more TensorFlow introduction. I hope the following courses will go into deeper details. Nevertheless, great course and very instructive.

교육 기관: Prerna D

•Sep 07, 2019

Very good course. All the concepts explained very well. I just feel programming assignments were too easy, they could be a little tougher

교육 기관: Yashika S

•Sep 10, 2019

tough one