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

4.9
stars
57,312 ratings
6,579 reviews

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

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.

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176 - 200 of 6,506 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Saad A

Oct 4, 2017

Post the first course, this course would is the one that is going to make you feel like a deep learning practitioner. You get to understand why deep learning is sometimes called an art how much difference in terms of speed and accuracy can be made just by tuning the hyper parameters. Highly recommended if you know deep neural networks and willing to dive deeper into them.

By Xun Y

Apr 7, 2019

Again a great course about deep learning. The course structure is very well defined, with step by step to build technical foundations in the beginning and later using open source deep learning framework to connect all the pieces together. Dr. Andrew Ng made all of them very easy to learn and sometimes I feel like I should jump out the comfortable zone he created for us.

By Willismar M C

May 22, 2018

Very nice course about important subjects of Vanilla Neural Networks, as optimizations algorithms , regularization methods, hyper-parameters used and how to implement them in practice. A very nice chapter on the sequence of the specialization that give me understanding on important aspects of it, how to use and how to implement them. I really enjoyed each detail of it.

By Bharath S

Jul 8, 2019

This course gives a very good idea of the overfitting problem in deep learning and different ways to overcome it. It also introduces commonly used optimization methods in deep learning. A nice introduction to tensorflow is provided in the last week's programming assignment. Overall it is a very satisfying course. Many thanks to the instructor and the entire team!

By Hari K M

Jan 3, 2018

Key course in the specialization and covers wide array of topics which are responsible for improving the DNNs. Complicated than the first course but very well explained by Andrew Ng. Things definitely get clear after doing the programming assignments. One should definitely complete this course if one has already completed the first course. I totally recommend it.

By Bilal A

Jan 12, 2020

Course was amazing, content was amazing, assignments was amazing.

Andrew Ng is the best teacher I have ever experienced in my life. I learned a lot from this course, these things are very difficult to learn from research papers it takes a lot of time but person with great passion of deep learning can learn all these things in just three weeks. Highly Recommended.

By Hiep P

Nov 29, 2017

In era of deep learning bloom, know how to control network model is an important thing. And this course has them all, from tuning learning rate to speed-up convergence or applying drop-out for avoiding overfit, etc... It shows you the under-the-hood theory and brings you the knowledge to grasp the basics yourself, and actually can apply back into your projects.

By WALEED E

Jan 8, 2019

The course is very useful for being acquainted with tuning hyper-parameters and modern optimization algorithms like momentum, RMSProp an Adam. It is also introducing how to prevent over-fitting efficiently from recent papers in addition to mini batching training data. Although it introduces TensorFlow in a brief way, the overall assessment needs some revision.

By Basel O

Nov 8, 2020

This course was as the first one. Nothing new, extremely interesting with Prof. Andrew. All assignments are such amazing. I like the way how they are formatted. It gives us a golden chance to revise theory and apply it immediately. Everything was just right. Thank you Andrew and TAs and all people who helped making this course looks the way what it looks now.

By Ruthuparna K

Jul 9, 2020

Gives you an in-depth understanding on how to finetune your neural network hyperparameters and introduces you to the various optimization methods. Finally, an introduction to TensorFlow gives a more practical solution to developing your code fast and easy. Yet again, Andrew Ng is nothing short of brilliant and his ML content is always the best in the world.

By 石啸

Feb 15, 2020

I strongly recommend this course since I pass an interview after finish the first and second specialization. Although it is not enough for some high-demanded company, it is a really good lecture and experience for the new beginner in neural networks. But I have to say that the project is too easy so far, I wish we will have more great exercises and projects!

By Saimur R A

Aug 2, 2020

This course trully go deeper into the deep learning and I learned a lot of things which improve my concept about NN network. Andrew gave an excellent lesson like the first course and simplify everything and the quote from Andrew "if you dont understand anythink don't worry too much about it" really make sense and over the time the concept will get clearer.

By Jaime A

Sep 8, 2017

Very clear, straight to the point, explanations with very well guided programming assignments in Python to hammer the concepts. A lot of knowledge and experience condensed in just a few hours and materials. I recommend previous exposure to Python and Machine Learning to make the most of this course (Ng's Coursera's course provides a very solid foundation)

By Amaranath B

Oct 13, 2019

This is an amazing course , the way they had designed the transition from numpy to tensorflow was amazing. The the concepts of gradient descent with momentum to adam optimizer was great coming from your previous course , I can't express how much this has grounded my understanding. I'm pushing myself to complete the specialization. Thanks a lot everyone !

By Naveen K

Sep 25, 2017

The course if very structured. Can't think of any improvement in course structure. Will like to thank Andrew Sir for this great effort.

As an improvement it would be great if people can be encouraged to solve problems on different dataset on internet such as kaggle. Such sources with other help can be provided as work to do after the completion of Kaggle.

By Daniel V I

Feb 9, 2020

A fine continuing of the previous course in this specialization.

Learning optimization algorithms to improve our parameters' update, how to normalize the inputs at each and every layer, how to prioritize certain hyperparameters over others when testing.

All culminating with Tensorflow, a platform that saves us a lot of time in programming Neural Networks.

By GAUTHAM M N

Oct 16, 2020

This one got pretty much quicker than the last course and was quite simple, though valuable inputs were provided by Andrew Ng. He is the coolest DL teacher i could have come across till now. Tensorflow is awesome and good basic idea was given in this course. Suggest it for everyone to go through. and I am running towards finishing this specialization.

By GAURAB B

Jun 19, 2019

Brilliant material altogether.. almost a compulsory course for researchers diving on the ocean of deep learning.. While I was reading papers on deep learning I came across all these terms but couldn't understand it.. Now the picture is pretty clear... Thanks Prof. Andrew Ng for this wonderful effort. I have already recommended this course to everyone.

By zhijun l

Dec 6, 2018

A great course talks about the detail in building Neural networks. With the first course as a foundation, student taking this definitely will get a better understanding on hyperparameter tuning and optimization, in addition on training neural networks. I recommend this course to those who would like to know neural networks more than just the concept!!

By shaila a

Jul 26, 2020

The details covered in the course are very important for pracical use. They are not commonly available on the Internet otherwise. Also, with the new libraries that make the task of coding easier, the knowledge of tuning parameters, of optimizing learning curves, is often overlooked. This course highlights the importance of that knowledge. Thank you!

By Oliver M

Aug 14, 2017

Having completed Udacity 730 on Tensorflow, I found Andrew Ng filled crucial gaps in my understanding. He is not afraid of presenting some maths to build intuition, but he always presents it in a straightforward way. Compare his explanation of Adam optimisation with the source paper on the subject. Andrew boils it down and serves it up beautifully.

By KOVVURU M K

Oct 29, 2020

One of the course I have ever taken. Taught me the nuts and bolts of Neural Networks. Now I feel more confident dealing with hyperparameter tuning. Before this course I am just doing trail and error method or grid search to find the hyperparameters with understanding why it work or didn't work. Now I understand what should be done to make it work.

By Adail M R

Sep 13, 2017

Once more, Prof. Ng show in his simple style how to tackle the tough subject of hyperparameter tuning, pointing to several techniques and helping us selecting the most appropriate ones for the task at hand. The Tensorflow introduction is also very effective and engaging! Looking forward to advance my knowledge and experience with the next courses!

By Diego A P B

Mar 6, 2018

Hyperparameter tuning and the other techniques seen in this course are not perceived to be the most fashionable areas of machine learning and deep learning. Nonetheless, they are crucial parts, and thus the techniques shown in this course will show you how to save great amounts of time and headache when trying to improve and finetune your models.

By K R

Jun 12, 2020

This course is very helpful in the matter of enhancing the knowledge from the previous course and getting the right intuitions about improving deep learning neural networks.

Thanks to Professor Andrew Ng for making it very clear and easy to understand and giving me the right tools for my Phd research .

I look forward to getting to the next course.