Chevron Left
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

4.9
stars
62,874 ratings

About the Course

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive 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 neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

CM

Dec 23, 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

Thanks.

NA

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.

Filter by:

3601 - 3625 of 7,219 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Abhishek S

•

Feb 18, 2020

very helpful must join and do all questions !

By Madhu M

•

Jan 1, 2020

A course to gain knowledge on hyperparamters.

By bala K K

•

Nov 22, 2019

Great Lesson, learned a lot. Thanks, Coursera

By fbrand

•

Oct 22, 2019

Just miss the notes at the end of the videos.

By Tej B

•

Oct 13, 2019

Learned a lot of about hyperparameter tuning.

By Kornel S

•

Sep 30, 2019

Great course, interesting and advanced stuff!

By Sami A

•

Sep 23, 2019

Great course .... introduced us to Tensorflow

By Sedat K

•

Aug 26, 2019

Great course for understanding Deep Learning!

By Rudra P

•

Aug 16, 2019

Thank you. It helped me a lot to learn a lot.

By Barka T

•

Jul 11, 2019

Really great and smooth transition tensorflow

By Simon T

•

Jul 3, 2019

Great as always when learning from Andrew NG!

By Yuanzhan W

•

Jun 12, 2019

A good walk-through of hyper-parameter tuning

By Shivam S

•

Jun 10, 2019

Very Detailed Course. Lots of stuff to learn.

By Khaled J

•

May 21, 2019

Excellent course with practical applications!

By Вадик Л

•

May 6, 2019

Mmmmm, very interesting and powerfull course!

By Samkit J

•

Apr 28, 2019

Great course. Excellent for concept building.

By Sangeet K

•

Apr 19, 2019

It's really a great course by a great mentor.

By Miguel A A U

•

Apr 3, 2019

Nice hints on how to improve DNN performance!

By barryhf

•

Apr 1, 2019

It is a privilege to learn from Professor Ng.

By Rajat C

•

Feb 18, 2019

Brilliant way of covering a complicated topic

By Robert M

•

Jan 29, 2019

Really enjoyed the last section on TensorFlow

By rajesh t

•

Jan 22, 2019

Very interactive and very clear explanations.

By Sarfaraz K

•

Jan 19, 2019

Very well organized course by a great teacher

By Uğurcan A

•

Nov 2, 2018

I have better intuition about Neural Network.

By Saurabh J

•

Sep 20, 2018

More practical real life examples would help.