Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI
About the Course
performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze
bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout
regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms,
such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a ne...
Top reviews
AM
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
DD
Mar 28, 2020
I have done two courses under Andrew ng and I am grateful to Coursera for their highly optimised and easily learning course structure. It has greatly help me gain confidence in this field. Thank you.
2201 - 2225 of 7,271 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
By ngoy m
•Jan 25, 2020
Learning quickly from very well thought out and put together lectures by Prof. Ng and team
By Sathvik B
•Jan 2, 2020
very practical course.... although hyperparameter tuning could be explained in more detail
By Tim W
•Dec 15, 2019
Thank Professor Ng for teaching us with such as a well-organized and very unique contents.
By Namburi S
•Nov 11, 2019
An excellent course with practical applications on how to do better hyper-parameter tuning
By Dmitry S
•Oct 18, 2019
Invaluable to understand the practical specifics of hyperparameter tuning and optimization
By Joe M
•Sep 29, 2019
Very nice course. Gives a real world perspective into optimization of deep learning models
By Anne G
•Aug 27, 2019
Thanks again for the great tutorials and the learning experiences! Thoroughly enjoyed it!
By Jerry
•May 27, 2019
Thanks to deeplearning.ai for this nice couse, really learned a lot and good for beginner.
By Saurabh S
•Mar 23, 2019
Awesome course. Improving the Deep Neural Network performance is explained so intuitively.
By Deepak M
•Jul 25, 2018
This course content is structured so well that you would almost never refer anything else.
By zhaoxi w
•Mar 18, 2018
very useful; i like the programming assignment which is well structured and well explained
By Jyoti K
•Mar 6, 2018
It is a great course to learn in-depth details of deep learning concepts. I learned a lot.
By Lanchen S
•Feb 10, 2018
I wish I could learn more about Tensorflow. But anyway, this course is already good enough
By Kimi T
•Feb 8, 2018
Excellent course. Provide a lot of useful tips to find the the optimal way to train a DNN!
By Jitesh J
•Sep 28, 2017
More programming exercises for Tensorflow may be advantageous from learning point of view.
By Ingrid A
•Sep 18, 2017
This course offers immediately usable advice for improving performance of neural networks.
By yanghui
•Aug 23, 2017
Programming assignment is well-designed to help me understand new concept;
great course!!!
By Vinid S
•Aug 14, 2017
Wonderfully structured course! Thanks a lot for your efforts to democratize Deep Learning.
By Антон В С
•Dec 30, 2020
Brilliant course to dive deeper into neural network and optimization processes inside it.
By Virat K
•Nov 15, 2020
It is an amazing method of learning and quite an interesting puzzle at end of the course.
By Aleksander Z
•Sep 20, 2020
Thanks for the great course! But I think the usage of Tensorflow v1 is a problem in 2020.
By Snow Y
•Aug 11, 2020
The optimization algorithms are very effective. The teacher explained clearly and easily.
By NESTOR M D M
•Jul 13, 2020
This is a great course, most of the content isn't available in books or in other courses.
By Yover M C C
•Jul 8, 2020
High quality Course, I have learnt a lot about optimization and deep learning frameworks.
By Ashish L
•May 12, 2020
Great course to understand the bolts and nuts to build a proper optimized neural network.