Chevron Left
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
40,776개의 평가
4,340개의 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

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 4,270개 리뷰 중 276~300

교육 기관: Johnson J

Mar 25, 2019

Awesome course! Andrew explained optimizers like RMSProp and Adam very clearly. I learned a lot!

교육 기관: Erick A

Mar 14, 2019

I strongly recommend this course who anyone is interested on understanding what is going on behind deep learning algorithms. The lectures are very instructive and impel one to learn more and more about this discipline.

교육 기관: Nilmadhab D

Mar 14, 2019

Very addictive course. A lot of fun. Prof. Andrew Ng is just outstanding.

교육 기관: Abhinay P

Mar 27, 2019

This is one the best courses. The use of Hyperparameters and tuning is best explained.

교육 기관: Ali A

Mar 25, 2019

This course of Professor Andrew Ng is as excellent as all the others of him. When I think about AI, he is the best.

교육 기관: Jeeva V

Mar 26, 2019

Excellent course in regularization, optimization techniques and how to use them with Tensorflow framework.

교육 기관: Ravikant C

Mar 28, 2019

I really enjoyed doing this assignment. A perfect combination of hands-on and concept discussion.

교육 기관: Lucifer Z

Mar 28, 2019

awesome!

교육 기관: Sanket G

Dec 20, 2018

Awesome

교육 기관: SW J

Dec 21, 2018

It's very much helpful. Thank you.

교육 기관: Armen D

Dec 19, 2018

This course fills many important gaps from the first course in the specialization.

교육 기관: Kavita G

Dec 20, 2018

Some of the details of the hyperparameters I wasn't aware and didnt realize the impact until I went through this course. Thank you.

교육 기관: Santiago I C

Dec 05, 2018

En línea con los anteriores. Muy teórico pero perfecto para entender los entresijos del funcionamiento de los algoritmos. Si acaso echo en falta algo más de tensorflow pero supongo que se verá en el resto de cursos de la especializacion

교육 기관: Srinath D

Dec 04, 2018

Another excellent course by Proffesor Ng. Lots of material to learn but neatly organized and extends the journey from the previous course. Lots of ML/DL terminology is clarified and their place and importance shown in this course. Very useful!!

교육 기관: 伟杰 邓

Dec 05, 2018

I will keep taking the following courses given by professor Ng

교육 기관: Amit K

Dec 04, 2018

This is good course for the student, who want to do real stuff with NN. Some of the tricks are well explained like L2,dropout, adam, momentum, minibatches etc. I think these are much needed tricks if i need to implement and tune my own NN on my own problems. I prefer to have a second level of such course which really talks about challenges in real life NN and how to solve those. Once again thanks alot for the entire Team for pulling this together.

교육 기관: DOMENICO P

Dec 05, 2018

Very well organized. The right balance between theory and practice with good hands-on examples you can exercise without boring details of language syntax...

교육 기관: Aleksa G

Dec 21, 2018

Really cool mooc, learned new ML theory and had a chance to implement it from scratch!

교육 기관: 朱荣鑫

Dec 23, 2018

As good as before

교육 기관: 杨志超

Dec 23, 2018

很棒:有理论、有实践。

再一次感受学习的魅力。

感谢吴恩达老师以及你们的团队。

也许,你真的帮助我改变了命运。

谢谢你。

교육 기관: KUNIHIRO O

Dec 22, 2018

very great useful. I want to learn compute science (bachelor's degree)by top 10 of university.

that Mooc is success. I want more learning

교육 기관: Mubashar N A

Dec 23, 2018

improved my knowledge and thinking abilities about hyperparameters tunning

교육 기관: Murat T

Dec 24, 2018

Topics cut in to sections are well defined and so clear. Programming assignments definitely gives you hands on experience. Also, math is demystified that you track with high school math. If you used framework like Keras and you want to know why and when you need to use that function,parameter etc., you would love this course.

교육 기관: Humberto d S N

Dec 07, 2018

Great explanation on Hyperparameters, Regularization and Optimization.

교육 기관: Rusty M

Dec 07, 2018

I learned a lot about the area that is not much talked about in deep learning, which is hyperparameter tuning! The forum was very helpful in debugging the programming assignments! Thank you Prof. Ng for the wonderful course. I thank Coursera as well for believing in me and granting me Financial Aid. It wouldn't have been possible without your help, Coursera Team. THANK YOU VERY MUCH! :D