学生对 DeepLearning.AI 提供的 Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的评价和反馈
课程概述
热门审阅
AA
Oct 22, 2017
Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course
NT
Aug 19, 2019
I think this course is great. Because we learn about some definitions about hyperparameters, optimization which are frequently appears in papers or in the functions in some Deep Learning frameworks.
1701 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 1725 个评论(共 7,283 个)
创建者 Ankesh K P
•Aug 27, 2017
Great content. Helped to clear concepts of very in-depth hows and whys i had been looking for a long time. Thanks!
创建者 Willy N
•Aug 24, 2017
awesome material. well explained concepts on hyper-parameter tuning batch normalization and programming frameworks
创建者 Noah W
•Aug 15, 2017
This course is a bit difficult.But the lecturer explained in great detail.I have learnt a lot.Thank you very much!
创建者 Shallom M
•Jan 1, 2022
This course has been wonderful. I learnt alot and the insights into the theoritical side of things was top notch.
创建者 Matt G
•May 27, 2021
Good follow up course to the first course. Begins to get into the practical aspects that surround neural networks
创建者 Kiril P
•May 13, 2021
Every ML engineer should know how to speed up learning process. This course was extremely useful and interesting.
创建者 Guru P B
•Mar 28, 2021
very impressive and includes a lot of things, it would be better if there is a case study to show all the tuning.
创建者 PATEL H V
•Nov 27, 2020
This course helped me to understand many concept thoroughly . Also, provided practical knowledge of each concept.
创建者 Teja S
•Oct 12, 2020
It is pleasure doing the course, it has beatifully curated lectures for fundamental understanding of the concepts
创建者 ANKIT M
•Jul 24, 2020
My whole expectation from the course meet! Thanks Andrew NG sir and Coursera to provide such an excellent course.
创建者 Arpad H
•Jul 5, 2020
I liked it very much :-) At least I used tensorflow and I understand what happens in it. I like Python very much.
创建者 Lokesh T
•Jun 30, 2020
It is such an amazing course. I learned a lot of thing during that entire course.The explanation is very awesome.
创建者 Sudarsaan A
•Jun 23, 2020
As always Andrew Ng's teaching is amazing which never fails to give us a intuitive understanding of the concepts.
创建者 Ayan C
•Jun 2, 2020
Exceptional course with a lot of in-depth look into the various hyperparameters used in deep learning algorithms.
创建者 Tan W H
•Jun 1, 2020
A really great course that explains most if not all the tunable hyperparameters in a typical deep neural network.
创建者 MOHAMMAD A U
•Apr 3, 2020
Excellent course. Pretty much in-depth knowledge provided. Thank you for the course.
Special Thanks to Andrew sir.
创建者 SEBASTIAN M G S
•Mar 8, 2020
Very interesting and good implementation in the Jupiter notebook.
However, Tensonflow introduction is very vague.
创建者 vanraj
•Dec 23, 2019
After completing the first course of this series, I feel very confident in applying Neural Networks in my domain.
创建者 Jordan S
•Sep 11, 2019
A great introduction to tuning neural nets. Exercises were extremely useful in explaining the techniques further.
创建者 Celia C
•Mar 27, 2019
Hope the tensorflow homework can be more clearly instructed. And hope there were more tensorflow part of homework
创建者 Varun N
•Mar 27, 2018
Very useful course which gives insight into the nitty gritty details of the practical aspects of neural networks.
创建者 Mansi G
•Dec 24, 2017
This course was much more challenging than the previous one. Go into it prepared to put in a lot of extra effort.
创建者 Bruce D
•Dec 10, 2017
Fantastic course, really jumps into important details for practical programming and implementing neural networks.
创建者 Manish Y
•Dec 3, 2017
Good course for understanding optimization of neural networks and their implementation. Introduces to TensorFlow.
创建者 Deleted A
•Oct 26, 2017
Recommend! I feel much better with this course than the first one, probably because I get more used to this area.