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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
63,157 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.

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

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3151 - 3175 of 7,249 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Sounak B

Jun 19, 2021

Wonderful course learnt a lot about tuning hyperparameters

By Maximilian G H

Feb 22, 2021

Good overview of more practical aspects for deep learning.

By Juan C J

Jan 22, 2021

Good insights on common practices to work with NN everyday

By Безруков А Р

Jan 20, 2021

As always, excellent explanations of the material. Thanks!

By Conrad L

Oct 6, 2020

Really good content. A bit challenging but not impossible.

By Ricardo S M

Sep 14, 2020

Excelent course with the important details of training DNN

By Lucas F

Aug 26, 2020

Amazing Course like always! Andrew Ng is a top instructor!

By Yaungni L L

Jul 26, 2020

This is a great course if you want to learn deep learning.

By Paras J

Jun 4, 2020

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By Maxence d B C

May 4, 2020

A bit redundant if taken after the Machine Learning class.

By prateek s

Mar 29, 2020

Great course. Covered most of the optimization techniques.

By Aditya K

Feb 23, 2020

Wonderful Content , great delivery and amazing assignment.

By Anshuman K

Jan 10, 2020

Well structured, very well explained ... Excellent course!

By Javier S A

Nov 19, 2019

Really good course to improving my skills in deep learning

By Gustavo d P P

Nov 8, 2019

very good course.

The explanations are in an efficient way!

By Javier F

Nov 7, 2019

Excellent! Very clear course. Congratulations to Prof. Ng!

By Madan K

Aug 21, 2019

Introduction to Tensorflow was systematic and educational.

By Chun Y Y

Aug 3, 2019

Interesting course to learn how to optimize your ML model!

By 介阳阳

Mar 16, 2019

Thank you for providing such an amazing course! Thank you.

By Ethan ( W

Mar 5, 2019

Great intuition to understand how to improve deep learning

By Ibrahim H

Nov 23, 2018

Very well balance between theory and hands-on assignments.

By 鞅骠豪

Dec 22, 2017

质量一贯的好但是就是有点短,希望能加长一下。还有能不能提供如何安装TensorFlow的视频知道我觉得这很关键!!!

By Mathias P

Dec 8, 2017

Thumbs up to Andrew Ng for making the complex more simple.

By Xiaoyang G

Nov 1, 2017

Really helpful course to learn how to tune the parameters.

By Hussein N

Oct 29, 2017

A wonderful overview of hyper parameters and model tuning