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
62,857 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

HD

Dec 5, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.

the only thing i didn't have completely clear is the barch norm, it is so confuse

XG

Oct 30, 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.

Filter by:

4151 - 4175 of 7,218 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Paul R

•

Aug 24, 2020

Concise, but very to the point.

By Ajay G

•

Jun 6, 2020

Excellent teaching methodology.

By Bhaskar J D

•

May 25, 2020

Excellent Content to understand

By Dr. V V R M R

•

May 7, 2020

very good inputs for TensorFlow

By Edward M

•

Dec 22, 2019

Another great Andrew Ng course!

By MURALITHARAN S

•

Dec 13, 2019

Nice Work, Good Learning For me

By 楊惇昱

•

Nov 8, 2019

Thank you. It's a great course.

By Bahalul K

•

Nov 8, 2019

it is very helpful to my career

By Habibur M

•

Nov 4, 2019

Easy to understand. Thank you..

By Brendan F

•

Sep 2, 2019

Great explanations and examples

By Harish M

•

Jun 8, 2019

Very good lectures by Andrew Ng

By 김화겸

•

Apr 27, 2019

Andrew ng.... i love so much...

By Andrew

•

Oct 30, 2018

非常感谢coursera提供这么好的课程，内容很详实，收获很大

By Bình B N

•

Sep 6, 2018

Very good tips. Clear advice :D

By Xuhaoshen

•

Jul 1, 2018

Very Useful Course in Practice!

By ChenKuan S ( S

•

Jun 17, 2018

可以很清楚地了解整個參數調整的涵義跟DL基礎理論與實務的銜接。

By YixiangWang

•

Jun 17, 2018

i learnt a lot from Ng. Thanks.

By yifan

•

Jun 11, 2018

Andrew is such a great teacher!

By Wenbo W

•

Jun 8, 2018

an useful course and thank you!

By 劍峰 劉

•

Apr 1, 2018

This course helps me a lot. thx

By debneil r

•

Mar 17, 2018

Learning TensorFlow was great!!

By Harsh S

•

Mar 13, 2018

Great. I love you Andrew Ng Sir

By Han K

•

Jan 19, 2018

excellent and insightful course

By Dharmik g

•

Jan 4, 2018

Great Course and great learning

By lixu

•

Dec 24, 2017

nice course, very appreciate Ng