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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 5326~5350

교육 기관: Alex N

Sep 12, 2017

Good pace

Only drawback is that some of the safe checks are wrong in the programming assignments, even with the right seeds.

교육 기관: Khalid A

Sep 15, 2019

It is definitely very informative, but I wish the lectures would be more in depth in regards to the derivation and proofs.

교육 기관: Ruud K

Feb 06, 2019

Really love the course, the quizes and programming assignments. But not 5 stars cause the audio quality is extremely poor.

교육 기관: Arkosnato N

Nov 28, 2017

course content was very good, but this course should be longer. there was a lot of material covered in a very short time.

교육 기관: Michael B

Mar 17, 2018

More pragmatic approach with theorems would be more appealing....or maybe it is me as i'd prefer Java (DL4J)...not sure

교육 기관: Santiago F V

Jul 02, 2020

The theorical part is perfectly explained. However, the program assingment of the las week is not as good as expected.

교육 기관: Tan N

Jun 22, 2020

Thanks a lot for clearly explaining of intuition about algorithms and optimizer. More ever, great design of assignment

교육 기관: Avinash K V

May 04, 2020

Outstanding material. Would like to thanks Mr. Andrew Ng Sir for providing such a nice and detailed description.

THANKS

교육 기관: Viviana M

Oct 29, 2017

I really enjoyed the classes, in the training I would've liked to try and improve the model with all the tools learned

교육 기관: Amit J

Nov 22, 2019

Great practical insights.

I wish there were programming assignments on "Hyperparameter tuning" and "Batch norm" too.

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: George L

Oct 24, 2018

it's good, but definitely not as good as the first course since Prof. Ng was not very clear on some of the concepts.

교육 기관: Ruixin Y

Apr 30, 2018

The course itself is great, but the notebook (programming assignment system) is not stable, it's annoying sometimes.

교육 기관: Peter T

Apr 17, 2018

Useful information, good intuition, but lack of formal results. More homework would improve the learning experience.

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: Suresh D

Mar 01, 2018

I hated the tensorflow part though. Would have much preferred it if we could have moved away from jupyter notebooks.

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: Abhinava K

Dec 08, 2017

Content is good, but assignments are not interesting. Some application oriented assignments will be be encouraging.

교육 기관: Manish C

Jan 23, 2020

Like all other andrew ng courses this course is also the best course to deep dive into neural network algorithms .

교육 기관: Francesco P

Feb 26, 2019

I would like to see more programming assignments. They are very well done and it'd be great to have more of those.

교육 기관: Angad P S

Dec 13, 2017

I would really benefit from this course if more assignments are provided to try different data sets and scenarios.

교육 기관: Rahad A N

May 13, 2020

Absolutely love the course and the way Andrew teaches us, though I have a little bit discomfort in writing codes.

교육 기관: Emmanuel

Mar 05, 2020

A little bit to theorical and with too many guidance at some points and not much at some other (for TF functions)

교육 기관: Giovanni C

Feb 11, 2019

I liked the course, but the explanation of tensorflow needs more propaedeutic introduction for a learner like me.

교육 기관: Charbel J E K

Jan 17, 2018

Really helpful ! Too much concepts to understand but only applying few in the course. I really liked this course.