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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
40,746개의 평가
4,339개의 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 4026~4050

교육 기관: 侯凌

Dec 02, 2017

Need slides and notes

교육 기관: Yue X

Sep 15, 2017

Programming assignments are too simple.

교육 기관: Alejandro R

Oct 26, 2017

I miss the end of video quizzes, but can't rate it lower than 4 because this course is excellent.

교육 기관: kartik c

Sep 05, 2017

There were a few mistakes in the output of the comments of the notebooks,Also sometimes my output did not match the expected output,still the assignment got graded correctly.Eg-The tensorflow notebook.I think it was because of the seed of the random processes.

교육 기관: Farzeen H

Mar 25, 2018

I would love to give 5 stars but I have reduced one because of the typos in the assignments. I 'managed' to waste my time to check my code many times as my answer was not matching the expected output. Later I figured out that there was an error in the expected output.

As a course, it gives a thorough understanding of playing around with hyperparameters and fine tuning the NN to get better accuracy.

교육 기관: Martin P

Dec 24, 2017

The course is well organized and I've learnt quite a lot related math knowledge. The only thing I felt need to improve is that the assignment was too easy and I can easily pass even though I didn't fully understand all the concept and details. Hope we can make it hard and more opportunities for the learner to make mistake and correct in order to learn more.

Thanks

Martin

교육 기관: David B

Oct 05, 2017

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교육 기관: 杨之龙

Apr 05, 2018

I have to complain why dont accompany videos with quiz and notes just like the ML coursera.

교육 기관: Alejandro E

Feb 19, 2018

Very good course, although it'd be awesome if Andrew went over the backprop associated with Batch Normalization and perhaps a programming example of using Batch norm on my test set.

교육 기관: Juan O

Dec 03, 2017

Having slides like in other courses will be helpful

교육 기관: shudhatma

Jun 17, 2018

A very good course

교육 기관: Mark M

Oct 30, 2017

The intro of hyper parameters was from mathematical point of view as good as the basics of week 1, however practical relevance becomes not really clear.

교육 기관: JaesungHuh

Aug 20, 2017

Good course

교육 기관: J K

Feb 10, 2018

better than the first course since it involved breaking into new stuff w.r.t the Stanford Machine Learning course.. However, altogether not yet challenging enough to give 5 stars

Really interested to go deeper into this matter..

교육 기관: Giordano S

Sep 28, 2017

Maybe not as exciting as the first course of this series (Neural Networks and Deep Learning) as this one delves more in the "technicalities" of NN. The presentation of the topics, however, is always very clear and easily understandable.

교육 기관: Ashutosh P

Apr 04, 2018

It was a great course. Really well taught by Professor Andrew Ng. Some "from the scratch" coding assignments needed.

교육 기관: Flaviu I V

Apr 07, 2018

I feel like the second course was better then the first one. But there are a couple of typos in some assignments and the assignments are still too easy.

교육 기관: Rafael M

Aug 31, 2017

Very good.

Would be better it it touches tools like keras.

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: Francisco C

Jul 24, 2018

Very good content overall. Very well explained and good examples. Many mistakes in the comments in the assignments.

교육 기관: Suman D

Jul 27, 2018

Awesome.

교육 기관: Rahul K

Jul 24, 2018

The best course in deep learning: Hyperparameter tuning, regularization and Optimization. The course is best among all the available courses over internet but it lacks availability of study materials (or reference to reading materials).

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: Łukasz Z

May 02, 2019

bugs

교육 기관: Aaron E

May 05, 2019

its a good intro, if not a little simplistic with the coding exercises, bring back the quizzes mid lecture