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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
42,913개의 평가
4,599개의 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

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

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.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,540개 리뷰 중 3976~4000

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: Jie Y

Mar 10, 2018

The class should include more introduction on the current ml frameworks such as tensor flow etc. Possibly it should include one more project for the ml framework. Hope to give students more experience on the ml frameworks.

교육 기관: Deva C R M

Nov 19, 2017

Good and detailed information on how to tune parameters, optimization techniques and regularization. I'm confident that this course learning will help me in training NN to better convergence in a shorter time than earlier.

교육 기관: Karl S

Jan 03, 2019

I would have liked more details on the math. Furthermore, I think that the discussion of TensorFlow was a bit too short. Although I was able to do the assignment I have not yet developed an understanding of TensorFlow.

교육 기관: Julien B

Jun 27, 2018

Excellent. Mon regret est que l'exercice final ne mette pas en oeuvre le tuning des hyperparamètres sur un jeu de cross validation. Un exercice supplémentaire avec TensorFlow ou Keras sur cette notion aurait été un plus.

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: 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: Heung K L C

Sep 23, 2018

Very exciting and interesting course overall but the programming assignment with Tensorflow was not practical in my opinion. Instead having practical experience building NN with Keras might have been the better choice.

교육 기관: Nikolay K

Sep 10, 2017

Generally the course is very good! I liked that I could manually implement the steps of hyperparameters tuning. I wish there was a bit less boilerplate code. Implementing everything from scratch would be more valuable!

교육 기관: Mark H

Mar 10, 2018

Could be Greatly improved by having us build a NN using previous learning's with the only change being use of SoftMax for Cost. Then have us use TF to do the same and compare the code effort, and the results 1-to-1...

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: Christoph D

Feb 03, 2018

Nice course, as always!

But I think the hyperparameter tuning methods are hopelessly outdated / missing the most promising current developments. A pity since this is such a central part of the actual work with DNNs!

교육 기관: Yuvini D S

Dec 10, 2019

You can get a better insight as to how to improve neural networks that go beyond the fundamentals. The quizzes and assignments helps you get a hands-on experience of the theoretical material covered in the course.

교육 기관: Oriel B

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: Craig M

Oct 21, 2017

You've learned deep neural nets but on the first problem you apply them to they seem to not work or learn to slowly. Don't panic, all you may need is a little fine-tuning, that is what this course will teach you.

교육 기관: Joakim P H

Sep 04, 2017

After this second course you will be able to start build things using Tensorflow. Really great to see how good this course is structured. Things from course one is comming back making it easy to grasp new content.

교육 기관: Gemeng Z

Jan 28, 2019

Overall, the course is interesting and introduces systematically technical details. There are still some confusing part in the assignment. For example, the direction in the last assignment is kind of misleading.

교육 기관: Amir H

Jun 25, 2019

The explanation and examples are very informative throughout the course. The quizzes and the assignments are highly related to the topics covered in the videos which provide a solid understanding of the course.

교육 기관: Luca V

Jul 25, 2018

Some very interesting consideration, though I would have liked a section about reproducibility and randomisation (including for GPU trainining), though I understand that this is framework and language dependent

교육 기관: Karl M

Nov 21, 2017

Some of the programming assignments are a bit confusing, and the grader seems to suffer from bugs at the moment. Nevertheless I found especially the part on optimization algorithms very helpful and interesting.

교육 기관: William R

Oct 02, 2017

The insights and intuitions Andrew communicates are good, but as he starts to point out towards the end of this course, in practice one uses a DL Framework and you don't code these things from the ground up.

교육 기관: Armaan

Aug 15, 2019

Extremely well designed course, the key reason for 4 stars is Andrew Ng's amazing leactures. The programming assignment though do quite a bit of handholding which can be reduced.

Amazing experience overall!

교육 기관: Haiwen Z

Jun 16, 2019

The course is great for beginners, and I'll recommend watch the vid with Deep Learning on MIT Press. The only cons for me is that subtitle is toooo big, I wish I can change the font size on the vid setting.

교육 기관: Gianluca M

Mar 14, 2018

Very short, but very interesting. Some more advanced topics are presented that students don't typically learn on coursera courses, such as improvements to gradient descent, batch normalization, and dropout.

교육 기관: Philip

Jan 15, 2020

Good course, not quite as intuitive as the first course in the specialisation 'Neural Networks and Deep Learning' but still very good. Its also great to have some exposure to Tensorflow through the course,