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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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6301 - 6325 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Ricardo A F

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Aug 13, 2020

The concepts were explained in a very understandable way. I would give it 5 stars if it treated the subjects in a deeper mathematical way and if the tensorflow version used was 2 and not 1.

By Oliverio J S J

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Jan 25, 2019

The course is interesting but I am not sure that the best learning strategy is to fill in some lines within a program. I am disappointed that I can not download the material for future use.

By Noah M

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Dec 10, 2019

With the basic knowledge I earned in course 1, it was very helpful attenting this coruse on improving Deep NN and I took a lot of notes during the course, to which can refer in the future.

By sai v

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Feb 13, 2019

Nice course for improving deep neural networks , they will show the all the paths available to improve a neural network , all you have to do is explore it based on your passion and need :)

By Shivank Y

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Jan 26, 2019

The course content is great but the ending lacks tensorflow implementation of regularisation, hyperparameter tuning, learning rate decay, etc. and aslo still not confident enough in those.

By Kai H

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Jan 22, 2019

The final programming task might contain minor bug, passed all sub-sections, but the final one result didn't match with the provided results, better provide more info for easier debugging.

By C. I

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Aug 31, 2017

Good material. The exercises are a little bit easy. The worst part is that after the last assignment, the certificate is done immediately and you don't have a chance to correct any errors.

By Miro A

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Jan 27, 2019

Excellent lectures, well prepared, very good examples, great teacher.

I would happily give it 5 stars, if not the constant issues with Coursera infrastructure, crashing notebooks/kernels.

By Anthony K

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Nov 8, 2018

The course is very interesting and fairly well laid out but some simple typos can cause some confusion and they have been there for a long time based on some info in the discussion forums

By Sandeep P

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Jun 24, 2018

Nice course. Great introduction to hyper parameters in neural networks and also nice assignment on tensorflow. It would have been even better if they introduced tensorflow in more detail!

By ZW

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Sep 2, 2018

Good material and some very nice practical tips. A few typos here and there in the course material made it difficult at times to debug the code, which is the reason for docking one star.

By Dany J

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Nov 10, 2017

Good covering of many implementation aspects of neural networks. I find the practical exercises to lean on the tedious side while not bringing a tremendous amount of learning themselves.

By Jose L M

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Sep 14, 2018

It was somewhat frustrating to spend so much time coding raw python, just to discover that TF can do all of that with one-liners. Nevertheless it was valuable to learn the nitty-gritty.

By Akhtar H

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Jan 21, 2021

Nice explanation of Tensor flow. Hyperparameter tuning was explained in easy and robust way. Programming Assignment is tricky but forum comments helped a lot in resolving the problem.

By Aditya L

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Aug 8, 2020

Some extra information on various optimization algorithms will be good. Moreover, if there are links to some of the research papers and resources to dive into, it will help out a lot.

By Tilman H

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May 10, 2020

Excellent course, but I did not learn many new things (some just from a different angle). Maybe the course description should be updated to be more specific about the target audience.

By Darvoftw

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Jul 7, 2019

Some very interesting material for beginners. At times it feels like concepts are being repeated over and over again, but there is enough new concepts to keep it worthwhile to repeat.

By Wahyu G

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Mar 10, 2018

Not so much different with the materials in the Machine Learning course from Prof. Andrew Ng itself. If you don't have the time to finish the ML course, then you should take this one.

By Shawn E

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Dec 19, 2022

Great content but there are major problems with the final assignment. The one-hot encoding function tests force the output tensor dims to be different than what a later cell expects.

By Md. A J

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Sep 29, 2020

The mathematical explanations were very good. But the coding task is always left to do at once. If it can be set after the corresponding videos as a module it would be great I think.

By Alejandro N

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Sep 8, 2020

It is an excellent course. The only weird thing it is that it uses Tensorflow 1 instead of 2. I get it why is it done, but perhaps it would have been more useful to keep using numpy.

By Jorge L M B

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Jun 24, 2020

Awesome material, and everything is well explained. I would've liked that the programming exercises were a little more challenging, though going through the code shines a nice light.

By Vishnupriya V

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Jun 22, 2019

As always Andrew Ng's clearly explains all the concepts along with practical programs. I would strongly recommend doing this course for a good solid understanding of neural networks.

By Ivan

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Mar 14, 2019

While video lectures are very well explain subject matter, practical assignments are pretty frustrating since most of the time you will be battling jupyter notebook and auto grader.

By Alejandro E

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Feb 19, 2018

Very good course, although it'd be awesome if Andrew went over the backprop associated with Batch Normalization and perhaps a programming example of using Batch norm on my test set.