Back to Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

4.9

39,278 ratings

•

4,171 reviews

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....

Oct 31, 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.

Dec 24, 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.

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By LEO L

•Jul 31, 2019

All is good except the submission part, sometime return submission failure without specifying a reason

By Oleksandr T

•Jul 29, 2019

Last code assignment is a mess. Looks like organizers have no intention to fix errors.

By Aayush A

•Aug 03, 2019

The Jupyter notebooks had a lot of mistakes which wasted a lot of my time otherwise the course content was good

By Shubham K J

•Aug 08, 2019

Grader is not performing well even though my outputs are matching.

By Dr. H H W

•Aug 08, 2019

Interesting material but a bit complex to follow all the equation derivation. Need to repeatedly watching the video to understand the content. After learning this the hyper parameter setting in the ML setup is clearer to me.

By Aymen S

•Aug 13, 2019

Cours intéressant merci beaucoup Mr Andrew Ng

By Aditya S

•Aug 09, 2019

good

By Gianluca S

•Aug 10, 2019

No course material available

By Armaan

•Aug 15, 2019

Extremely well designed course, the key reason for 4 stars is Andrew Ng's amazing leactures. The programming assignment though do quite a bit of handholding which can be reduced.

Amazing experience overall!

By Laurence G

•Aug 11, 2019

Decent intro to tuning neural networks. I felt the parts on normalization and regularization could have gone into more detail, but perhaps the math was deemed too complicated. Labs are ok, but still a bit buggy despite errors being reported in the forums a while ago.

By ATIK M

•Apr 25, 2019

Good can be improved by providing more code based video like Tensorflow.

By Efthimios K

•Jun 13, 2019

Good but need letter recognition NN to understand what he writes

By Asad A

•Aug 17, 2019

Great videos but wish there were more per-lesson exercises that were there in Course#1 for this track. Also, the transition to TensorFlow was quite abrupt as the key concepts that TF uses are completely new and don't easily borrow from the much cleaner Numpy concepts

By Mukesh K

•Aug 19, 2019

The content of the Course is very precise and assignment truly reflect what is been taught in the lectures. Explanation and presentation of algorithms are what I like the most. Assignment were very engaging and interesting.

By Prerna D

•Sep 07, 2019

Very good course. All the concepts explained very well. I just feel programming assignments were too easy, they could be a little tougher

By José D

•Sep 07, 2019

This is Course2 of the Deep Learning Specialization. In Course1, we learned how to code the algorithm in Numpy. Most of Course2 show how to optimize and tune the algorithm and how to use and tune the hyper-parameters. Most assignments are well-designed and easy to perform as they focus more on the understanding than "finding how to code it". However, the last assignment introduces TensorFlow where we re-implement the algorithm using TensorFlow concepts. I have to say I expected TensorFlow to simplify things but it turns out I find the Math/Numpy implementation way easier to understand than TensorFlow. I'll have to dig deeper in TensorFlow concepts to understand it better. I would have liked more TensorFlow introduction. I hope the following courses will go into deeper details. Nevertheless, great course and very instructive.

By Gerrit V

•Aug 19, 2019

Sometimes quit slow

By Nguyễn H T

•Aug 20, 2019

I think this course is great. Because we learn about some definitions about hyperparameters, optimization which are frequently appears in papers or in the functions in some Deep Learning frameworks.

By Hossein M

•Sep 09, 2019

too complicated, many lessens in couple of short videos.

poor video transcript

By Yashika S

•Sep 10, 2019

tough one

By Saurabh D

•Sep 12, 2019

Insights about how machine learning works in real life is quite ingeniuos.

By Roy W

•Sep 13, 2019

Great course on hyperparameter tuning. Some of the code projects used the same variable names repeatedly in different contexts, which, to me, at least, is a bad practice to encourage in students. Also, in the Tensorflow project, some additional numerical calculations would have made it easier to catch issue earlier. But Andrew Ng was amazing, as always - clear and informative.

By Marc D

•Sep 14, 2019

The course really takes the student by the hand through the exercises. The disadvantage is that it is not really necessary to understand what you are doing. Just follow the guidance. But on the whole really satisfactory

By Daniel E B G

•Aug 26, 2019

I think this course would benefit from a little more explaining. There are a lot of new concepts and some explanations were too quick in my opinion.

By Gopal M

•Sep 14, 2019

TensorFlow is a bit nebulous.I need more practice.

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