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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
별점
52,739개의 평가
5,974개의 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

AS

Apr 19, 2020

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

NA

Jan 14, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 5,911개 리뷰 중 5251~5275

교육 기관: Kate S

Mar 07, 2018

Excellent material! There was one error in the last assignment that cost me a lot of time. Please fix that. Otherwise, very useful programming assignments.

교육 기관: SUNIL D

Jul 07, 2019

Very Good Course to understand Step by Step

Hyperparameter tuning, Regularization and Optimization to improve Deep Neuaral Networks & Practical Assignments !

교육 기관: Hanqiu D

Aug 10, 2020

Great course and great teacher. The skills in this course is very practical. But I think the assignment should use tensorflow version 2 instead of version 1

교육 기관: Zachary Z

Mar 26, 2020

Learned a lot about tuning and different frameworks. Definitely math-intensive and more of a brief overview than a deep dive of these techniques and tools.

교육 기관: Rekhawar N N

Dec 14, 2019

improving Deep Neural networks :Hyperparameter tuning,Regularization and optimization course was amazing! thank you so much coursera and Andrew Ng sir! :))

교육 기관: Alex C

Sep 24, 2017

Please offer a lecture note in detail instead of just ppt shows for each class video, not to mention that some are missing which is inconvenient to recap.

교육 기관: Muhammad B A

Jun 25, 2018

Great material and lectures. Would've preferred slightly more comprehensive exercises though, and more on tensorflow(any deep learning framework) as well

교육 기관: Francois-Xavier

Dec 18, 2017

The tensorflow programming assignment was a little too easy. It turned out to be more or less of a copy paste work without having to look at the TF docs.

교육 기관: Behrad K

Jul 26, 2020

The content was perfect but last programming assignment was excruciating! But I thank everyone involved in making this course, it was unbelievably good!

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: 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: Stephan W

Sep 02, 2017

As always - excellent lectures by Andrew Ng. However, I think that the programming assignments tend to be a it too easy and a bit too much "copy/paste".

교육 기관: Sergey

Oct 06, 2019

I wish prof. Ng provided more intuitions into underlying math particularly why gradient optimization techniques help. But like it anyways, very useful!

교육 기관: Anthony

Nov 08, 2017

Great material, few minor errors that need fixing throughout. Noted in forums. I expect this will improve as more take the course and feedback applied.

교육 기관: Hair P

May 17, 2020

This course has to be updated!!!!! TF 2.0 is what we are using now, and especially for new users, it is important to start from the newest frameworks.

교육 기관: Isaac S

Nov 27, 2019

I missed in the course an explanation and possibly a programming assignment of different tuning algorithms, such as random search and Bayesian search.

교육 기관: Rajeev D

May 25, 2020

The coverage on the subject was adequate but it will really help to make a pdf supporting document to highlight the hyper parameter tunning approach.

교육 기관: James D B

Jun 23, 2019

Probably a little too follow your nose at this point in the specialisation. But none-the-less very good. Would give 4.5 stars if that were an option.

교육 기관: Christoph S

Mar 03, 2019

Still some flaws + inaccuracies + video sequences that should be cut out. I think the organizers should really do it as people are now paying for it!

교육 기관: Teodor C

Dec 28, 2018

Last Tensorflow assignment has some output typos and bugs when using operators like @ and +. Course was ok, but that assignment took me way too long.

교육 기관: HongZhang

Jun 14, 2018

Great course to deepen my knowledge after first course. However, I would like to access more programming exercise for practice. That will be perfect!

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: Stephen R

Oct 26, 2018

Enjoyed this course, especially the material that goes a bit deeper (different optimization methods, parameter tuning) and the intro to TensorFlow.

교육 기관: Huang C H

Nov 24, 2017

Less exciting than the first course, but this course is important to understanding the parameters that could affect a neural network's performance.

교육 기관: Youssouf B

Apr 22, 2019

what I did recognize in the deeplearning specialization that there are now further reading suggestions or reading syllabus like the other courses.