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

4.9
stars
62,820 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

AS

Apr 18, 2020

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

AB

Aug 26, 2021

Amazing course which focus on the theoretical part of parameters tuning, but it needs more explanation of Tensorflow, as I felt a little lost in the last project. Except that, it is an amazing course.

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6276 - 6300 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Avi v

Jan 3, 2021

This was a great course....but at some places I felt that the details have been hided a little....only in few videos.........but overall it was a great course.....best of the courses...I have ever seen ..

By Ashwin A R

Jan 27, 2020

This course helped in deepening knowledge about optimization techniques and how they could make ML/DL algorithms robust while training. This also provides a good introduction to the Tensor flow framework.

By Charles H

Nov 8, 2019

The lectures are all really good, but the programming assignments feel like they hold your hand too much. It's very easy to sort of slide through them without having a good understanding of the material.

By Aditya K

Mar 22, 2020

Everything till now was good, But I can't tell why my forward propagation method is rejected although it matches the expected output. So my marks were deducted for it without any reasonable explanation.

By Vu N M

Oct 25, 2018

A bit boring with this course at the first sight, but later when you work with the real system, this course can be a bible for you. The valuable experiences from Andrew Ng are shared through this course

By Gillian P

Mar 2, 2018

Though very good, his course might be a little less polished than the previous. One more week diving into frameworks would (maybe keras to see a more functional level of Framework) would be appreciated.

By Manoj A

Apr 18, 2018

There was no exercise on hyper-parameter tuning so the course seemed incomplete. I think week 3 should be split into 2 weeks with first week focusing on hyper-parameter tuning and second on TensorFlow.

By Øystein S

Oct 22, 2017

Ng is an excellent teacher, and it was fun to learn about programming frameworks. However, the programming exercises are very simple, and the videos about numerics go very slow, thus 4 stars and not 5.

By Benjamín V A

Jun 1, 2020

Very good course, useful and smart. Some of the example are on tensorflow 1 but I think that they will update them soon to keras tf2 Thank you!

I will pass on what I have learned here to undergrads :)

By Yan L

Feb 21, 2018

very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

By Ashim

Oct 23, 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

By Hans J

Jun 11, 2020

great and practical insight. carefully crafted assignments. still coding in python and the quirks coming with it are sometimes of equal difficulty if not worse than understanding the explained theory

By Kevin C

Dec 19, 2019

Excellent content. The grader seriously needs to be updated thogh. For example, it needs to be Python2 and Tensorflow2 compatible and also needs to be robust in handling common syntaxes such as "-=".

By mitch d

May 5, 2018

Would have liked to see the math and more complete explanations for all the things that Prof. Ng glosses over by saying "you don't really need to understand XYZ". Even if this material was optional.

By Ravindranadh R

Jun 12, 2020

Could have increased assignments and some more indepth knowledge of tensorflow and proper installation way of tensorflow cause mine is showing error when iam trying to practice as shown in the video

By Nguyễn H T

Aug 20, 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.

By Rajeev s

Jun 22, 2019

very good course with deep knowledge of each parameters. Little bit stretched at tensorflow. A bit of overview on tensorflow API and tensorflow architecture could have been better before exercises.

By Sankalp B

Apr 11, 2020

Great teaching from Andrew Ng as always. Would've loved to learn the math behind optimization techniques, but nevertheless Andrew gave intuitions of the algorithms which cleared up a lot of stuff.

By Peter F

Nov 6, 2018

few minor errata within the assignments that haven't appeared to be fixed even 1 year after reported. But otherwise learned a lot and enjoyed the course style and will continue to learn this spec

By Charles-Edouard C

Dec 6, 2017

one remark for the last assigment, the neural network to read the hand signs, once finished and validated , I tried with my own pictures from my webcam, and it never worked (always predicting 2).

By Patrick S

Sep 18, 2017

Having a good understanding of tuning the Hyperparameters is key to build powerful neural networks.

The course helped me to keep a focus on tuning and understanding the relationships parameters.

By Mark P

Jun 26, 2019

very quick moving but the assignments were too easy - they give you too much of the code (both the surrounding code which is fine but also the precise code for running optimisers for example.

By John R

Jun 16, 2020

This course helped me a lot to clear my confusion regarding various Machine learning jargon of words. It gave a intuitive understanding and helped solidify my foundation in Machine Learning.

By Tianhao C

Oct 5, 2019

I like this course a lot! 4 star due to the programming assignment. It is well designed, but hope the assignment could be more challenging instead of just giving us a taste of deep learning.

By Ayham S

Aug 26, 2022

There were a couple of bits of maths that weren't fully explained and the very final programming assignement definitely had missing explanations but otherwise was really engaging and useful