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Back to Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

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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6426 - 6450 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Giuseppe N

Jul 9, 2018

It's very good, but I would have spent more explaining the difference between adding layers and adding neurons, and how to decide the next move.

By Jeremy Z

Dec 11, 2017

a few of the examples and expected output for the programming exercises seemed not to be correct. otherwise great course. highly recommended.

By David A S

Sep 27, 2017

Good course. Kinda skips over hard bits which only leaves one with more questions. Hopefully these details are recovered in the later courses.

By 지혜성

Apr 18, 2021

Very good class. Appreciate it.

However, the explanation for some theories is not enough.

More explanation needed for Adam optimizer, RMS prop.

By Dinh T T

Feb 9, 2019

It's a wonderful course because it provides me how to improve deep neural networks and delve to some techniques to gain good hyperparameters

By John S L

Feb 1, 2019

Would have given 5 stars if the Jupyter exercise did not give me too much of a hard time looking for errors in syntax. Overall, great lesson!

By parag p

Oct 19, 2018

Loved the easy to understand explanation given by Prof. Andrew Ng for some of the most complex concepts in Deep Learning like Regularisation.

By 김대희

Nov 5, 2017

This class is very helpful for understanding parameters of ML except week 3 class and assignment for Tensorflow which is not fully explained.

By 2K19 / E / A G

Sep 12, 2021

The TensorFlow part of the course could have been more in depth, because there were lots of problems faced during the programming exercise.

By Xiaochao G

Dec 25, 2017

I don't understand tensorflow mechanism and when to use what function. Should I stop to learn more tf or just move on the following courses

By Tuấn T L

Nov 9, 2021

The video content and theories went very how. However, week 3 assignment has some bugs and unclear explaination of compute_cost exercise.

By Nataliia K

Oct 27, 2019

Quite ok, but programming assignment was mostly copy-paste style. I am not able to repeat something similar independently after the course

By Maximilian B

Sep 25, 2018

A lot of great concepts covered in the lectures but only few were explored in the assignments. The assignments seemed fairly simple to me.

By Vanja T

Sep 24, 2017

There were grading results that seemed wrong - I've submitted report on grading to explain details. Other than that, the course was great!

By Batuhan A

Jun 17, 2020

This course was nice for me.First Andrew Ng talks about mathematicall background of the concepts then you get hands on coding experience.

By Aditya S

Oct 5, 2019

Good course. However expected some more mathematical proofs for some of the ideas like bias correction and exponential weighted averages.

By Prerna D

Sep 7, 2019

Very good course. All the concepts explained very well. I just feel programming assignments were too easy, they could be a little tougher

By Shreya A

Feb 16, 2021

It might help the academic learners if tutorials can be more engaging and rigorous than they are at present. But hey, not bad at all! :)

By Mohamed M A M

Jul 14, 2020

It's really great Mr/Andrew has a good way of explaining stuff even tho i need to search some stuff on youtube for greater understanding

By 2445_Nupur S

May 19, 2020

I loved the course, as it provided concise explanations and covered all important topics required in Deep Learning. Thank you Andrew Ng!

By Sidharth W

Oct 19, 2018

Would have been 5 star but I found typos in the assignments and exercises -which have still not been corrected which is quite surprising

By Styvens B

Aug 26, 2021

Good course overall . The batch normalization explanation were not so convincing. The last assignment on Tensor Flow need improvement.

By HARSHUL G I 2 - B

Aug 13, 2021

The course is nice but the tensorflow exercise has a lot of functions that weren't explained before and implementing them was difficult

By Varunraju V

Aug 24, 2020

Its good hands-on course but to master it will certainly requires to dwell more into the specifics and need to work on various projects

By Samarth B

Sep 11, 2017

Was a great course. Learnt conceptually and implemented Momentum,ADAM & rmsprop. Wish there were more exercises to explore TensorFlow .