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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

55,922 ratings
6,398 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

Jan 13, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

Oct 8, 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

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226 - 250 of 6,324 Reviews for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

By Kazi M R

Jun 6, 2018

From this course I have learnt several important hyper parameters, regularisation and optimization of deep neural network. Most importantly I got my first hand-on experience on Tensorflow framework by which creating deep net modes are quite easy if someone knows the elements of a deep net. I wish I will proceed for next course.

By Ian C

Oct 1, 2017

Learning about TensorFlow is brilliant. It's very hard to get a good understanding of what goes on in TensorFlow without fully understanding the neural network coding setup. This course beautifully combines the two. There were some minor frustrations with the final TensorFlow programming exercise, but overall this is excellent.

By Jeroen M

Jan 10, 2018

Great course, a few rough edges in the exercises and I also feel the exercise comments give away a bit too much (would be better if the student needed to figure out things by himself a little more). But these are minor details, I've learned a great deal in an amazingly short span of time, from one of the top minds in AI today!

By Jeff R

Oct 2, 2017

I appreciate the large amount of time that has gone into preparing this course. I note that there are a large number of corrections in the errata forum that have not been reviewed by staff. In particular there are some obvious errors in the programming assignments that could easily be corrected with a small investment in time.

By Pranshu A

Aug 22, 2020

Awesome, Amazing, Best, Fabulous course. Learnt a lot about Hyperparameter tuning and atlast got an intro about tensorflow framework which i wanted to start learning but earlier afraid from it's complexity which is explained so good in this course. Best course for Tensorflow Intro and Hyperparameter tuning in Neural Networks.

By Murat T

Dec 23, 2018

Topics cut in to sections are well defined and so clear. Programming assignments definitely gives you hands on experience. Also, math is demystified that you track with high school math. If you used framework like Keras and you want to know why and when you need to use that function,parameter etc., you would love this course.

By Gilles D

Sep 5, 2017

Eventually a clear and definitive explanation about Network initialization, regularization and optimization. Good insight share on hyper-parameters prioritization.

We learn the how and why and suddenly, it all becomes a little bit less mysterious. It is all clearly explained in a very accessible way.

Great value for my needs

By Xuefeng P

Aug 29, 2017

This course really gives you a fundamental and practical ideas about the hyper-parameters of DNN, and the way of tuning them. The part I liked most is the last programming assignment ---- play with Tensorflow!!! The assignment walks you through Tensorflow structure and basics in a very organized fashion.

Highly recommended!

By Ali n

Jul 7, 2020

Best course for learning Hyper parameter tuning, Regularization and Optimization topics further more the batch and other various optimization algos like momentum, Adam, Batch norm etc are much easier here. Please have a look on ground reality too, Take quiz before staring any course that this candidate is suitable or not

By Mihai L

Jan 21, 2018

This course is also interesting. The art of tuning hyper parameters and other optimization techniques are very interesting and nicely explained.

The introduction to Tensorflow and assignment is also interesting.Overall the difficulty is not high but the concepts are really powerful and important ,most scaffollding is done

By Vlad M

Sep 7, 2018

The course part is overall good.

The last assignment can be improved in two key ways:

The comment # Z3 =,Z2) + b3 should be # Z3 =,A2) + b3 - figured this out by myself without help from forums. :)

Also, the Adam optimization is not very apparent in the instructions - searched in the forums for issues.

By Evandro R d P J

Oct 22, 2020

Another wonderful course of this amazing specialization. I could say a lot of things, maybe even pages on how Professor Andrew it's the right person to teach you about Deep Learning but I'll shorten in this review and recommend the whole specialization for you! It's worthy and there's a lot of knowledge to be shared!

By Brad M

Aug 22, 2019

In my deep learning classes in academia, hyperparameter tuning was always "hand-waved" away - my questions were always deflected, or put off. This class answered every one of my questions, and made me more confident I'd be able to implement a DL system in industry, and be satisfied with the results. Very good course!

By Zeinab B

May 4, 2020

This course will cover everything you need regarding your neural network performance. I always had questions on why and when you use Adam, SGD, etc. and after this course, I have a much better understanding of how to choose hyperparameters and optimization methods. I highly recommend this course to ML practitioners.

By Toby K

Nov 1, 2019

I am working through the DL specialisation. Consistently good teaching style and the programming assignments are suitably pitched for getting the learner to pick up methods quickly e.g. Tensorflow syntax for self-application later. Good course and looking forward to the next in the series. Well done Andrew and team.

By Ankur T

Nov 21, 2018

word is not sufficient signup and experience it. For a deep learning beginner who already have math background can easily understand concept behind it but for implementation you need to refer extra materials on internet and book too. Andrew Ng explain only concept and recipe but for practice you will struggle hard.

By afshin m

Feb 5, 2018

This course is continuation and a requirement of the first course. Really like the learning style of how first course and the first 2 weeks of the second course taught neural networks by doing all the math and calculations manually and finally introduced Tensorflow with parallels of what was taught in the class.

By arulvenugopal

Dec 17, 2017

This is another excellent course in this specialization. I enjoyed the programming assignments. The instructions, tips made Tensor flow coding section to be easy . However, few blocks consumed more than few hours, due to placeholders. logic and the TF documentation is overwhelming. I am proceeding to next course.

By Wei L

Aug 26, 2017

This course is harder than the previous one. It teaches more details of tuning parameters and optimization in deep learning. In the end it also teaches tensorflow which is really helpful. It's like a programming course, nerally all the commands have been already provided, so it's not hard to get the code correct.

By Muhammad T

May 26, 2020

As usual, Andrew is a great instructor. He taught very complex concepts in very simple language and used notations that were easy to understand and were consistent throughout the length of the course. WOULD DEFINITELY RECOMMEND. I am hoping to complete the specialization in less than a month. 2 down, 3 to go!!!

By 姜云鹏

Nov 20, 2017

It is really good and teach me the basic understanding of DeepLearning back propagation and gradients optimization like Momentum, RMPS, Adam finally I learn how to use Tensorflow to train my model.

But there are some mistakes in the assignments and also in the grade so that it costs me a lot of time but useless.

By Mushfiqur R

May 3, 2020

It was a good course on understanding various hyper-parameters, some regularization method, optimization of algorithms, various gradients and gradient checking, batch - mini batch, exponentially weighted average , some tuning algorithms and finally a small introduction to deep learning frameworks. RECOMMENDED!

By Vinodh R

Nov 12, 2017

The course content was excellent. The only issue is that there were some glitches with the grading of the second week programming assignment, in that I could obtain the expected output, but with repeated submissions, there would be (different) sections which could not be graded due to unnamed technical issues.

By Altaf K

Apr 6, 2020

5/5.Thank you sir for helping me in my career.I recommend everyone to go through this course if you really want to learn detail about hyper parameter tuning , optimizers and regularization used to make neural network better. It helps to open black box of Neural network and know in detail about how all works.

By Renato L

Jul 3, 2019

Excellent content and very well explained. Thanks for this amazing course.

The course cover the building blocks of a Neural network. Andrew (and his team) did a great job by organizing the content in an evolving way in which you have the chance to build the knowledge from each piece of a (deep) Neural Network.