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學生對 deeplearning.ai 提供的 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 的評價和反饋

4.9
40,716 個評分
4,334 個審閱

課程概述

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....

熱門審閱

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

HD

Dec 06, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse

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4176 - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 的 4200 個評論(共 4,268 個)

創建者 Kartheek

Feb 01, 2019

week 3 topics would have been a bit better

創建者 Amit C

Feb 01, 2019

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

創建者 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

創建者 Morisetty V A S K

Jan 20, 2019

Interface for evaluating is not great and assignments are easy

創建者 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

創建者 Long H N

Feb 13, 2019

N/A

創建者 zhesihuang

Mar 03, 2019

good

創建者 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.

創建者 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.

創建者 Ilkhom

Mar 21, 2019

awful sound

創建者 Salim S I

Aug 12, 2018

Would have liked programming assignment in python to understand the various initializations and optimizations. Although tensorflow introduction was good, It felt like being left stranded without a python assignment to cement the things learnt in the class.

創建者 Ashvin L

Aug 25, 2018

The course builds up on the first course and provides some ideas on how to tune the networks to perform better. However, at the core, I find the number of parameters overwhelming and it appears that by changing the parameters we can get any answer we want. There is no "formal" and mathematical basis for changing the parameters. This is a bit disconcerting.

The assignments were trivial. More importantly, at least one assignment appeared to indicate that the results are entirely dependent on weights chosen (at random) on the first iteration. This should not be the case.

創建者 Peiyu H

Oct 12, 2018

Lots of error on the final exercise. It seems some errors exist from previous sessions already. Hope the teaching team will fix the errors and make learning less confusing for us.

創建者 Jérôme C

Oct 14, 2018

Need more training on Tensorflow, imho

創建者 KimSangsoo

Sep 18, 2018

괜찮음

創建者 K K R

Sep 17, 2018

Some of the videos are very abstract and needs a bit of mathematical intuitions. These intuitions are best obtained by calculations rather than a lecture :)

創建者 Gadiel S

Sep 21, 2018

The course is good. It covers important ideas, and they are well explained in the videos. However, the formulation of the assignments is sloppy. There are mistakes and inconsistencies, in some cases necessary explanations are missing, and in some cases the instructions are misleading (I suspect the assignment has changed over time, but the instructions have not been consistently updated).

創建者 William K

Oct 01, 2018

I thought the content was well-chosen and typically presented clearly. However, unlike the previous course in this specialization, the assignments had an egregious number of typos and missing information. I found these errors confusing and time-consuming.

From the staff's forum activity, it looks like they are no longer actively involved in this course. I hope that Coursera will hire someone—an intern would probably be plenty capable—to take this course and carefully fix as many of the errors in it as she or he can find.

創建者 Ha S C

Oct 29, 2018

A much sloppier and poorer course than previously. Grading mishaps (on the fault of the grader), a few errors in the lectures (the variance in the normalization), and very basic and unhelpful feedback from staff made for a course that did not live up to the level of the previous one. If at any point you need further help, it is generally unavailable, or difficult to find at best.

創建者 Imad M

Nov 04, 2018

Week 1 and week 2 needs more examples of python programming in the videos. The videos for week 3 were a lot more interesting. Without the python implementation examples in the videos, the course can be very dry.

創建者 CARLOS G G

Jul 14, 2018

good

創建者 Juan J D

Sep 11, 2017

tensorflow subject was to superficial

創建者 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.

創建者 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.

創建者 harmouchi

May 06, 2018

ike usual andrew ng perfect explanation simple go to essential stuff.

the minus points some troubles with notebook

big thanks for andrew ng's team.