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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
52,522개의 평가
5,940개의 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

CV

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.

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 5,871개 리뷰 중 5276~5300

교육 기관: Kevin K

Jul 06, 2020

The course was great overall, but I wish we had more involvement in the set up of the programs in the programming assingments.

교육 기관: NAMAN M

Jun 03, 2020

It would've been much easier if the graded functions of the tensorflow practice assignment would've been explained separately.

교육 기관: Dário P

Mar 18, 2020

Nice basics that help understand the fundamentals. The last part of the course should be upgraded to Tensorflow 2.0 already...

교육 기관: Uddhav D

May 28, 2019

Again awesome explaining by Andrew, although I feel Batch Normalization should be a bit stressed upon and explained in detail.

교육 기관: Tarun S

Sep 19, 2017

Well detailed course..

Tensorflow is very basic and it could have been improved if one can visualize graphs too in tensorboard

교육 기관: Pranay V

May 20, 2020

Explains neural nets like a tutorial more than a formal course. Overall pretty insightful. Also, instructor can speak louder.

교육 기관: Steven A

Nov 07, 2017

Great course!! The only improvements I'd suggest is more difficult assignments (less guided) and more written documentation.

교육 기관: Alonso O O

Apr 08, 2020

This course was a little bored for me. I already knew a little bit about hypertuning so I felt that the course moved slowly.

교육 기관: Omar S

Oct 29, 2017

Provides a good code skeleton to build a neural network, but would unlikely have one poised to do improvements on their own.

교육 기관: Om S P

Jul 19, 2019

Some assignments, even though I get the same result as the output given, it get marked as wrong... Please try to rectify it

교육 기관: Victor P

Oct 26, 2017

Very good course from the excellent Andrew Ng.

Some typos and some glitches in the video, hopefully it will improve in time.

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.