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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

39,861개의 평가
4,245개의 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


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.


Apr 06, 2018

Fantastic course! For the first time, I now have a better intuition for optimizing and tuning hyperparameters used for deep neural networks.I got motivated to learn more after completing this course.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,179개 리뷰 중 4076~4100

교육 기관: André M

Oct 24, 2019

4* only because the TensorFlow lectures and assignment were too much in too little time. Also from what I see, TF has massively changed syntax to 2.0 so it felt a bit pointless to learn TF1 syntax (which is ***horrible***) at this point. To me it detracted a lot from the learning experience.

The remaining lectures and modules were excellent as usual though. I'd still recommend this highly, and Andrew's insights into what tends to work and why are brilliant as always.

교육 기관: Christopher S

Oct 25, 2019

Good intro to the available tools. Very guided course. For concepts to really stick, own projects or courses needed.

교육 기관: Sajal J

Oct 28, 2019

Very good course.highly recommended

교육 기관: yesid a c m

Oct 28, 2019

hay funciones de tensorflow que ser[ia adecuado que las explicaran en los notebooks.

교육 기관: Nataliia K

Oct 28, 2019

Quite ok, but programming assignment was mostly copy-paste style. I am not able to repeat something similar independently after the course

교육 기관: Andrew L P

Oct 29, 2019

Wonderful course, would have liked another assignment working with TensorFlow.

교육 기관: qiaohong

Oct 28, 2019


교육 기관: Nono

Oct 31, 2019

Thank you,Andrew

교육 기관: Mihaly K

Nov 06, 2019

Assignments sometimes too easy, minimal input needed.

교육 기관: thib c

Nov 07, 2019

more intuitive insights would be helpful

교육 기관: Charles H

Nov 08, 2019

The lectures are all really good, but the programming assignments feel like they hold your hand too much. It's very easy to sort of slide through them without having a good understanding of the material.

교육 기관: Vikash C

Jan 28, 2019

Content was good.

But the system that checks our submitted our code checks wrongly even when I wrote it correctly.

In week 2 assignment, when I submitted the code, it gave many functions as wrong coded.

I resubmitted the code after few changes, for instance a+= 2 changes to a = a+2 and string text like 'W' changes to "W". It worked fine and gave 100 points.

In short, what I observed is that the code checking system is taking a+=2 and a=a+2 as differently, also 'W' and "W" are considered different, but they are not in actual output.

교육 기관: Morisetty V A S K

Jan 20, 2019

Interface for evaluating is not great and assignments are easy

교육 기관: Kartheek

Feb 01, 2019

week 3 topics would have been a bit better

교육 기관: Amit C

Feb 01, 2019

I wish the course mentors were more active on this course makes it a bit difficult to clear doubts

교육 기관: srinivasa a

Jan 09, 2019

its great foundational course but i feel with frameworks available the math behind it was little boring.Andrew NG is pretty good with explaining it well but sometimes felt it was too trivial

교육 기관: Long H N

Feb 13, 2019


교육 기관: zhesihuang

Mar 03, 2019


교육 기관: Jorge G V

Mar 07, 2019

The lessons are good, the programming assignment has mistakes that have apparently been reported over a year ago and have yet to be fixed - there is no excuse for this to be the case.

교육 기관: Till R

Mar 02, 2019

Exercises are too easy, and lectures are kind of boring. The Jupyter / iPython system does not run smoothly. I ended up downloading everything on my local computer, completing the assignment there, and then pasting the code into the coursera notebook. That makes the assignments take 50% longer than necessary.

교육 기관: Ilkhom

Mar 21, 2019

awful sound

교육 기관: Tan K L

Jan 26, 2019

I think more should be done regarding the TensorFlow framework with more explanations given to what the functions did

교육 기관: CARLOS G G

Jul 14, 2018


교육 기관: Navaneethan S

Sep 20, 2017

This course was much less rigorous and theoretically-grounded than the first. There didn't seem to be much justification for any of the techniques presented, which was a stark contrast to the first course.

However, the topics are important and useful to know, so I'm glad they were covered. To me, the most useful sections were on softmax regression and deep learning frameworks, which I really enjoyed. The TensorFlow assignment was also interesting and (relative to the others) challenging.

I think there is a lot of scope for this course to be improved and I hope Dr Ng and team will do so in the near future.

교육 기관: Nikolay B

Dec 05, 2017

Lessons are nicely explained

Assignments should be more challenging. Same as first course, this one basically make you cope-paste instructor notes and just change variable names to pass all assignments.