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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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451 - 475 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Camilo M

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Jun 23, 2020

Not long ago I have been working with machine learning and artificial networks, but without a doubt so far I think my learning curve has been exponential. Themed content is for anyone to fall in love with technology and climb on the shoulders of giants.

By Akash R

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Dec 16, 2018

I understood the bits and pieces of how tuning particular hyperparameters will lead to a great improvement model being developed. Andrew NG was great to teach everything with examples and deep dive into the concepts. Thanks for the opportunity provided.

By Ravi K

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Dec 19, 2017

Great course on practical techniques to tune hyper parameters. Great to see the practical experience driven lectures with succinct, focussed theory. Learning has never been so exciting. Kudos for the team who put together such as great code scaffolding.

By Keith T

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Oct 28, 2017

Andrew Ng picks up where he left off with 'Machine Learning' and takes a relatively complicated subject and breaks it down into clear an understandable blocks, providing insight and intuition along the way. But that's what great teachers do well, right?

By Myunggwan C

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Feb 17, 2019

I'm on the road to improvement with my deep learning skills with the current specialization.

Thank you for providing such a great quality course online.

I also appreciate the mentors who comment to every post in discussion group.

Keep up the good work!

By Ankit M

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Nov 13, 2017

Very wonderful and thought provoking stuff has been provided for learning optimization and regularization. Latest stuff have been used to demonstrate the examples. Thanks to Coursera for providing a good platform to learn all this tools and techniques.

By Akella N

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Aug 29, 2020

This is a fabulous course for hyperparameter tuning, regularization, normalization, optimization, and other tensorflow framework commands. I have gained immense knowledge from this course. My deep regards to Andrew Ng et. al. for such a worthy course.

By govind b

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May 9, 2020

This course is good for those who want to learnt about the different regularization technique and the most important optimizer algorithms. The course material is good and easy to understand. I liked this course so much and it teach me lots of things.

By J.-F. R

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Feb 18, 2020

Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it.

By Rohit G

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Sep 18, 2017

In-depth learning about Hyper-parameter Tuning, multiple Regularization and Optimization Techniques. This Course makes learning Deep Learning Framework like Tensorflow very easy.

All thanks to Andrew NG and team for building this course so interactive.

By Nikesh P

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Jan 22, 2019

Hyperparameters can affect our parameters and how tuning them properly would speed up our optimization is nicely taught. And it was great to know the intuition and mathematics behind other optimization algorithmswhich which was also taught very well.

By MONIL J

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Jun 10, 2020

It's been a great learning through out this course. Even by improving hyperparameters, tuning them and optimizing them, we can increase the efficiency and reduce the execution time very much.

Thank you so much Andrew Ng sir for this amazing course!!!

By Victor

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Jan 16, 2020

This course is very useful, a good extension of the first deep learning course. Like the name of the course, on top of the basic neural networks knowledge, this course focus on how to improve the performance of the fundamental neural networks built.

By Navy X

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Sep 24, 2017

In the course there are lots of impressed optimization strategies that are hard to get outside from here. I was shocked when I saw the performance improvement after I run He initialization and Adam, according to the instruction in the course. Great!

By Okta F S

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Mar 24, 2020

I'm very happy can finish this course. After taking this course, I understand how to optimize deep learning model, how to tuning hyperparameters. Also, by doing the programming assignment I can understand how to do optimization process from scratch

By TANVEER M

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Jul 12, 2019

THE COURSE TAUGHT ME DIFFERENCE BETWEEN RMS ,MOMENTUM AND ADAM OPTIMISER.WHY BATCH NORMALISATION IS IMPORTANT.HOW REGULARISSATION AND DROPOUT IS USEFUL.GRADIENT DESCENT AND MINI BATCH DESCENT AND WHY IT SHOULD BE BETWEEN 1 AND NUMBER OF M(SAMPLES).

By Ivan L

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Apr 29, 2019

This course was a great introduction to more advanced topics of deep learning. I was primarily interested in the second week of this course, where different optimization techniques like momentum and optimization algorithms like ADAM were discussed.

By Raj

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Sep 15, 2021

Had some issues with the last tensorflow assignment, since these are guided exercises wasn't able to debug my code properly (cost function was giving me a lower value, documentation didn't help me debug it either, will be looking into it further )

By Scott P

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Apr 14, 2018

Great knowledge on how to optimize hyperparameters and example code is always appreciated. I appreciated the knowledge on effectively using sklearns hyperparameter search methods and knowledge on how to use tensorflow to create my own gpu methods.

By Filipe C d L D

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Jun 4, 2020

Excelente curso que explora o ajuste fino dos hiper-parâmetros, Regularização e Otimização dos modelos de Deep Learning. Recomendo fortemente este curso, pois o professor possui uma excelente didática e os exercícios são muito claros e objetivos.

By Jason M

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May 28, 2020

The course material was very good. The only issue I had was that the system seemed to go down once in a while, and I would have to exit and sign back into Coursera. Restarting the kernel didn't help. This would sometimes cause me to lose work.

By ashu a

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Apr 20, 2020

considering the topics that are covered those are very good and detailed and as a beginner those are required to be known to every individual , especially professor andrew has put a lot of efforts in designing the course it seems , it's very nice

By Geoff L

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Aug 29, 2019

Thanks to the instructor and the team for creating an excellent and rigorous online course. I particularly enjoyed learning the ADAM optimizer from the ground up. The quizzes and programming assignments are also challenging and fun. Thanks again!

By Gilberto B

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Jan 21, 2018

This course teaches how to organise training in a structured and systematic way without doing things so complicated. Also very good introduction to tensorflow a library that any data scientist that is serious about deep learning should now about.

By Mohamed E

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Dec 29, 2017

Very useful course which helped me to organize my thoughts about how to select the model hyperparameters, how to extended the network for multiple outputs using the softmax function. Additionally the Tensorflow lab was very helpful as jump start.