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Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

4.9
stars
62,825 ratings

About the Course

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

XG

Oct 30, 2017

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

JS

Apr 4, 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

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7001 - 7025 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Luis E O

May 17, 2019

Excelente

By IURII B

Apr 3, 2018

Thank you

By MD. E k

Apr 30, 2020

was good

By Suman D

Jul 27, 2018

Awesome.

By Davit K

Jul 13, 2018

easy bb

By 刘倬瑞

Nov 2, 2017

helpful

By Suraj P

Jul 17, 2020

Great!

By SUMIT Y

Jul 4, 2020

NICE!!

By qiaohong

Oct 27, 2019

作业过于简单

By Sonia D

Jan 30, 2019

Useful

By DEEPOO M

Jul 18, 2020

great

By Johannes C

Aug 29, 2017

Good!

By Pallavi N

Jun 26, 2022

Nice

By Aditya S

Aug 9, 2019

good

By Łukasz Z

May 2, 2019

bugs

By Aakarapu S P

Jul 3, 2018

good

By Dheeraj M P

Feb 23, 2018

good

By Darwin S

May 19, 2022

ok

By Alexandru I

Jan 31, 2022

ok

By Mohamed S

Oct 20, 2019

e

By Joshua P J

Jun 7, 2018

I've loved Andrew Ng's other courses, but this course was boring and not well-organized. The lectures were unfocused and they rambled a lot; they're nearly the opposite style of Prof. Ng's other material, which I found extremely well-organized. Most topics could be shortened 33-50% with no of clarity.

The course structure itself could use improvement:

The first part of Week 3 (Hyperparameter Tuning) belongs in Week 2.

The third part of Week 3 (Multi-Class Classification) should be its own week and its own assignment and could really be its own course. This is *THE* problem that almost every "applied" machine learning paper I've read is attempting to solve, whether by deep learning or some other class of algorithms. (Context and full disclosure: I'm a Ph.D. Geophysicist and my research is in seismology and volcanology.)

The introduction to TensorFlow needs to explain how objects and data structures work in TF. It really needs to explain the structure and syntax of the feed dictionary.

In the programming assignment for Week 3, there are three issues: (a) The correct use of feed_dict in 1.3 is completely new and cannot be guessed from the instructions or the TF website, and it's not clear why we use float32 for Y instead of int64; (b) In 1.4, "tf.one_hot(labels, depth, axis)" should be "tf.one_hot(labels, depth, axis=axis_number)". (c) In 2.1, the expected output for Y should have shape (6,?), not (10,?).

By Francois T

Jun 30, 2020

As an old school (80s) software developer I feel uncomfortable about the lack of formal teaching on the structure and principles of TensorFlow. Sure, I can write the code and fly through the programming assignment, I "kind of" get it, but for a thorough engineer, that "kind of" creates a sense of unease. I wish Andrew Ng, being the incredible practical teacher he is with the theory of Machine Learning, would have spent a bit more time reviewing that particularly practical topic of TensorFlow more in depth, because 1h on it would bring much more value than say, understanding the inner working of batch norm, especially to an engineer ready to onboard a new project and start creating. For example, when should you use a placeholder vs a variable and why? Why is there a "name" parameter in the constructor of a variable, when should I make good use for the difference between the name at a tf level and its actual Python variable name? etc... Unlike Matlab or Numpy, TensorFlow looks to me like it could use a bit more theory before practice. Next class? :)

By David C

Jul 22, 2019

Nice explanation of Adam. Extremely minimal introduction to tensorflow; I felt unprepared to deal with all programming error messages I encountered when using TF. I would have liked to have had more exposure to softmax outputs as well; the multi-class case is new here. My biggest complaint is that there was quite a bit of time spent trying to explain batch normalization and no corresponding programming assignment. Also, in the past I felt I had my hand held a little too much in the programming exercises, whereas when tensorflow was introduced I felt I'd been thrown by that hand into the abyss; the expected output could not help me debug because it seemingly was designed to remind me over and over that tf.Session.run was needed to give value to tf variables. ya... I think you guys have some work to do on this course.

By Todd J

Aug 18, 2017

Very mixed feelings about this course. The course title and nearly all (but 20 minutes) of the video content are on the topic of hyperparameter tuning, regularization and optimization of neural nets. This material is excellent. However, the programming assignment for Week 3 is about building a simple model in Tensorflow, with no coverage the rest of the material from the week. It is as if they included the wrong assignment, or just forgot to include the appropriate assignments to practice the actual content of the course. In addition, the Tensorflow intro in the videos and the Tensorflow assignment are not that great an introduction to the concepts behind Tensorflow. There are much better tutorials available on the web, such as from Tensorflow.org and codelabs.developers.google.com

By Evan M

Jul 31, 2020

Please update course to use / teach tensorflow 2 syntax

Also this course... really holds your hands through the programming exercise. The code in each exercise is well organized into separate subfunctions, each of which has its own check, so its already simple enough to debug. But when this is couple with the fact that the instructions basically spell out what lines to put where (and give significant hints as to what functions to use in those lines...) the whole thing is completely trivialized. I think that I at least would have learned much better if I was forced to use man pages etc. to look up the usage of functions, for example.