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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

55,852 ratings
6,391 reviews

About the Course

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization....

Top reviews

Dec 23, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

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 6,317 Reviews for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization


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.


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.


Jun 18, 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.


Oct 28, 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.


Jun 6, 2020

All the topics are very understandable, the way Andrew sir describe a concepts is just awesome. During the first specialization course i.e Neural Networks and Deep Learning , I was very confused about the hyperparameters tunning (like how to know what to chose). Khan Academy has helped me a lot to understand the underlying mathematical concepts.

By Nestor H

Jun 5, 2018

It was a great course to take. I could grab basic knowledge on TensorFlow and on some optimization techniques. I consider all the optimization algorithms are based on gradient descent, it is just that they tweak some parameters, but they are gradient-descent like algorithms. In summary, Dr. Ng is a genius and it is worth taking all his classes.

By Jay P G

Dec 30, 2019

After knowing the basics of Deep Learning and Neural Networks (From the course 1) , this course explains the crux of improving and tuning of the neural networks and it's parameters and Hyper parameters . And the intro to tensor flow at last was just awesome(not exaggerating it!!!) . Congrats to Andrew and his team for such an awesome course .

By Shivdas P

Dec 24, 2019

This course extends what has been taught in the preceding course, especially the different hyper parameters and optimisation strategies. Getting started with TensorFlow in a complete end-to-end example has been one of the things I was looking for and this course puts all that and many other things into perspective. Thanks Andrew and team !!

By Tamas K

Aug 3, 2019

The course was great, thank you! However, I'm really looking forward using Tensorflow in C++ or Swift. The obscure, untyped nature of Python facilitates cargo-cult habits, creates some mystic fog around the variables (since it's not explicit if e.g. 'cost' is a concrete float or an entire computation waiting to be executed) and error-prone.

By Eulier A G M

Aug 31, 2019

The course is very well structured, most of the topics here is perhaps kind of boring due the lack of real-problems projects, but if you stick to it and learn the concepts, will boost your understanding when using Deep Neural Network Frameworks, such as Tensorflow. That makes creating DNN easy to set, understand and apply to your problems.

By Suhas P

Sep 21, 2017

Introduction to TensorFlow was wonderful. This course has helped me visualize and experience end to end flow of an actual machine learning project that helped a lot. Thanks to Andrew for taking efforts to design the course in a user friendly way. Programming tips are intuitive, helps save your time and allows you to focus more on learning.