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.
About this Course
Learner Career Outcomes
Approx. 15 hours to complete
Learner Career Outcomes
Approx. 15 hours to complete
- 5 stars
- 4 stars
- 3 stars
- 2 stars
- 1 star
TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION
Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course
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.
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
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.
very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.
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.
Fantastic course! For the first time, I now have a better intuition for optimizing and tuning hyperparameters used for deep neural networks.I got motivated to learn more after completing this course.
Just as great as the previous course. I feel like I have a much better chance at figuring out what to do to improve the performance of a neural network and TensorFlow makes much more sense to me now.
Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.
Excellent content. The grader seriously needs to be updated thogh. For example, it needs to be Python2 and Tensorflow2 compatible and also needs to be robust in handling common syntaxes such as "-=".
Would have liked to see the math and more complete explanations for all the things that Prof. Ng glosses over by saying "you don't really need to understand XYZ". Even if this material was optional.
This was a fantastic course. Andrew Ng explains these concepts so clearly and provides practical examples that enhance the learning. Thanks again. I just loved the course as well as its predecessor.
This course is a big part of the meat of the Deep Learning specialization. I found both lectures and exercises gave me valuable practice at grappling with the actual process of training neural nets.
Excellent course. Bit tougher than first course. For those who have done Machine Learning course earlier and wondered that first course feels almost similar, second course is the 'real' next course.
A further step in the various tuning possibilities, and of course the introduction to TensorFlow. Feel confident of applying different tuning techniques and playing around with optimization choices
Having a good understanding of tuning the Hyperparameters is key to build powerful neural networks.\n\nThe course helped me to keep a focus on tuning and understanding the relationships parameters.
very good course with deep knowledge of each parameters. Little bit stretched at tensorflow. A bit of overview on tensorflow API and tensorflow architecture could have been better before exercises.
Phenomenal 2nd course in the DL specialization. The implementation notebooks really drill into you how the internals of Neural network training work: the forward/backward/update/regularization etc.
This is a very important part of the deep learning course, Generally people skip such type of things but here it is deeply explained and a hand on practice assignment makes it totally transparent.
Thank you! Great lecture videos and programming assignments with a lot of help built in. I was able to figure them out. Need more practice to master the materials. Thank you! This is a great start
Andrew NgCEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian KatanforooshLecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
About the Deep Learning Specialization
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.