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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
40,668개의 평가
4,331개의 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.

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 4,262개 리뷰 중 3976~4000

교육 기관: Guy K

Sep 22, 2017

Well organized !! clear explanations !

교육 기관: Carlos M

Feb 27, 2018

Great for the most part, but the TensorFlow assignments felt flat and "incomplete." I ended up using Hands-On Machine Learning with Scikit-Learn & Tensorflow to bridge the gap.

교육 기관: Joseph A D

Jan 13, 2018

Great course. Thanks for making it available.

I would have enjoyed more tensorflow lectures to help understand the underlying mechanism of the platform. I suppose the intention is to provide that understanding through the assignment, but more discussion in the lecture would be nice.

교육 기관: Kumar V

Oct 16, 2017

Good course could have been better expected little more on Tensor flow exercise.

교육 기관: Alberto S

May 20, 2018

By itself, not really a couse. It should be part of the first one.

교육 기관: Julian F

Sep 30, 2017

A very practical hands-on study.

교육 기관: Idan P

May 23, 2018

Add more theory

교육 기관: Jayanthi A

Apr 05, 2018

It was great course, however, I would have liked it to be a lot slower with more time being spent on Tensorflow.

교육 기관: Joakim P H

Sep 04, 2017

After this second course you will be able to start build things using Tensorflow. Really great to see how good this course is structured. Things from course one is comming back making it easy to grasp new content.

교육 기관: Venkatraman

Mar 10, 2018

Quite not challenging in the programming assignments

교육 기관: Enrique C M

Oct 19, 2017

Very good course about more critical concepts when building deep neural networks. Although the material seems like quite condensed and forced in a very short span of time (3 weeks) while the easier and more basic concepts in course 1 where explained at the right pace during 4 weeks.

These are core concepts and techniques for practical day-by-day deep learning engineering and programming and I would have wanted them to not to be taught in such a rush.

Even though, highly recommended course... I am already building real networks to solve real problems in some projects I am involved in and that is simply awesome after 2 courses of this specialization :)))

교육 기관: Jason A B

Oct 01, 2017

Great course for in-dept understanding of parameter tuning and optimization, +tensorflow. I would recommend increasing the complexity of the programming assignments. At this point we should be controlling more of the basic python setup.

교육 기관: Swann C

Oct 06, 2017

Good material and definitely essential in order to gain a lot of time aiming at the right direction navigating all these parameters

교육 기관: Chen X

Mar 27, 2018

It's fun they assume you know human error rate or optimal Bayesian. It's very rare in real world.

교육 기관: C. I

Aug 31, 2017

Good material. The exercises are a little bit easy. The worst part is that after the last assignment, the certificate is done immediately and you don't have a chance to correct any errors.

교육 기관: Leonid M

Oct 05, 2017

It seems the major part of this course is taken from the original "Machine Learning" course.

교육 기관: Karthi C

Jun 26, 2018

Became hard core technical but that's what it mean to be.

교육 기관: Wurihan

Mar 05, 2018

有点难懂,不过通过编程练习依然学到很多东西。

교육 기관: Imran P

Oct 04, 2017

I'd like a little more focus on tensorflow, perhaps starting at week 1.

교육 기관: Dixing X

Oct 16, 2017

programming assignments are too easy XD

교육 기관: Ugo N

Oct 10, 2017

It's okay. It's get a bit hairy with all the notation and varied intuition, but it follows suit and is not impossible to understand! Thank you Dr. Ng, I look forward to more.

Ugo

교육 기관: Arnav D

May 22, 2018

Best TensorFlow tutorial I have seen so far

교육 기관: Stuart R

Mar 11, 2018

Good course. Minor errors/typos in presented videos.

교육 기관: Miaoyin W

Oct 02, 2017

Need some improvement! I think the course is a little bit rush, especially on the 3rd week. I really like the 'test' assignments, which helps me to clear out a lot of important concepts. But the programming assignments sometimes bothers me not in the way of programming, but in the way of

교육 기관: Ganesh M S

Mar 31, 2018

The quality of the information is awesome. There are some minor bugs in the assignment section. Even though you have submitted the right answer it shows that you have secured 0 marks in that section. Apart from evaluation bug this course it super knowledgable.