Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization に戻る

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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 06, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse

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by Shabie I

•Feb 18, 2018

Concepts buried deep in technical jargon and seemingly complex mathematical notation are laid out bare for everyone to understand.

Mr. Andrew Ng is a very special teacher. The humility and down-to-earth character also add immense value to the course. He makes you believe truly that you too can do it.

by Brandon E

•Sep 26, 2017

An excellent continuation of the series. I particularly liked the in-depth discussion of Adam's optimization and the introduction to TensorFlow at the end of the course. The course does a great job of targeting specific concepts with practical advice related to tuning and optimization on real models.

by Kwan T

•Sep 28, 2017

It is amazingly rewarding to learn from Andrew, who is able to articulate so much insights into so many complicated refinements of Deep Neural Networks from so many different research papers. The Tensorflow programming assignment is one of best tutorials I have seen. Thank you for your great effort.

by Nityesh A

•Oct 08, 2017

Andrew Ng gives a good satisfactory explanation of the techniques covered in this course. He explains when to use the technique, how to use the technique and how one can implement it in Python and then goes on to give an intuition behind it. I think it should work well for newbies (worked for me).

by Eddie C

•Feb 18, 2019

My second AI course certificate from Andrew Ng after I left Taiwan AI Labs. Even though it took me more than 2 months to complete because of my kids' winter vacation and Chinese New Year break. I did learn a lot about how to tune and optimize a Deep Learning network. Keep going to the 3rd course.

by Shah M D

•Jan 20, 2019

Great Course. This course does explain some optimisation algorithm with quit a good detail. That is a good part of it. Many less courses explain those algorithms at a level of abstraction an undergraduate student needs. Also, it shows the usage of tensorflow, which is used by major practitioners.

by Hoang T H

•Oct 26, 2018

I think it's a great course for those who want to learn about technics related in Neural Network and don't want to know the mathmatical underlying too much, or for those who want to get an intuition or a picture about Neural Network. Thanks Dr. Ng and Coursera a lot for giving me a great course.

by Muthu R P E

•Feb 03, 2018

Very good course. We learn the basics of Machine learning and Neural Networks in the earlier course. It works fine when we work with the examples given here, but in real world, our basic program does not work. The tuning process is more important for a successful model. Thanks to Prof Andrew Ng.

by Steven M

•Mar 11, 2018

I felt like this course picked up specific problems and I was guided through them very well. Including theoretical aspects into the program assignments helped me to understand the concepts as I applied them! I also liked the funny comments every now and then. Great highly recommendable course!

by Gopikrishnan M

•Dec 07, 2017

This is a beautiful course that builds on the first one, it gives all the intuitions about various hyperparameters and makes us implement all that in python. Then he when we start working with tensorflow, it all makes sense because we actually know what is going on in the functions that we use.

by Samuel Y

•Oct 31, 2019

Incredible course. Very comprehensive, and goes over some awesome, industry-relevant optimization algorithms. Clear examples, programming assignments are extremely helpful, etc. Only things to improve would be to increase the difficulty of programming assignments, and focus more on Tensorflow.

by Andrei N

•Sep 21, 2019

The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.

by Mahmut K

•Nov 30, 2018

This second course was great in terms of showing improvements. I would have enjoyed a little more rigorous treatment of why improvements work, but then the course could go on and on... I sill think Andrew can spend a little more time on overcoming overfitting. All in all, excellent balance!

by Daniel J D

•Aug 20, 2018

It's a great course like the others and quite valuable. I am not quite sure how tensorflow fits into optimization, but I was glad to get a good, handholding kind of introduction to tensorflow as in these courses, I had become accustomed to doing things directly using numpy or MATLAB/Octave.

by Intan D Y

•Aug 17, 2018

This course helps practitioner or beginner to know how to tune supporting parameters in order to achieve more efficient/accurate NN. In other words, this course helps me figuring how to optimize the NN design, and I think this is recommended for beginners who like to explore Deep Learning/NN

by Nachiket R A

•Apr 15, 2018

This course provided a lot of insight in how to improve accuracy by tuning hyper parameters and also introduced multi-class problems and Deep learning programming frameworks! Awesome specialization to have as it aims to create well rounded expertise in Deep Learning and Neural networks area.

by Vaibhav y

•Dec 12, 2019

Coursera is so amazing to provide an opportunity like this to someone who is living in a 3rd world country with almost no opportunities in a high entry barrier field like Data Science. It is inspiring to see what coursers stands for, providing a learning opportunity to everyone, everywhere.

by Sinan G

•May 08, 2018

Nice breadth and depth of relevant topics in this course. Andrew Ng is as always very precise about the issues presented and helps build up our knowledge step-by-step in a super structured way. Nice to work with both Python (semi-raw) models and getting a similar introduction to TensorFlow.

by Xiang J

•Oct 25, 2019

Really like the assignments in this course, which gives me hands-on experience with advanced knowledge such as Adam optimizer, gradient checking. Tensorflow v1 assignment is also good, but I am not sure whether API is still relevant as Keras based API for tensorflow v2 is already released.

by Tarush S

•May 16, 2019

With this course, even the beginner can understand why what happens when tuning and optimizing a neural network model. With easy to understand methodology and great explanation, I highly recommend this course for anyone who wants to go deeper into deep learning and understand the workings.

by Meghdad P

•Aug 06, 2018

Very helpful learning material.

I'm still a bit confused though, even after passing the exams and exercises, but I think its mostly because I've lost grasp on mathematics. So, the blame is on me not coursera.

Hopefully I would fit more in the Deep Learning world by finishing up the course ;)

by Millard A C

•Feb 10, 2018

This is a great course and you get to do real programming and training of a Deep Neural network. Andrew Ng is an excellent instructor. The final assignment wasn't hard but the syntax was difficult to follow. Using the forum and the Tensorflow documentation you can make your way through.

by Bill T

•Feb 04, 2018

This builds on the basics from the first course with some important techniques (such as Xavier initialization, Adam optimization, and batchnorm) and ends with an introduction to implementing these in TensorFlow. Fast-moving but well taught with a good mix of theory and hands-on exercises.

by Sari S

•Jul 25, 2019

I am totally enlightened by this course. A lot of the concepts covered were completely new to me and very helpful in building a good performing neural network. The lectures were in depth and very well organized. The contents are not something you will come across in other tutorial sites.

by Bryan W

•Jan 18, 2018

A great refresher to Andrew's original ML course at first, but also later is learning current deep learning current mindset at work. Great pace, great course, and great programming assignments. Makes me want to see the 3rd course for (i hope) more challenging programming assignments :) .