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Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

4.9
stars
63,295 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

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

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.

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3076 - 3100 of 7,270 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Douglas C

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Dec 28, 2019

Well structured. Maybe too much help on the coding exercizes?

By Amish T

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Dec 18, 2019

Excellent course. It helps in fine tuning your neural neteork

By 曾湘

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Nov 25, 2019

nice design to clearly show the principle of neural networks.

By JackyChung

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Nov 23, 2019

Thanks for Coursera, I have learned so much from this course!

By anantharamaiah k

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Sep 19, 2019

This course gives excellent in site on Hyperparameter tuning.

By Michael Z

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Aug 19, 2019

The experiences from the teacher will help the starter a lot.

By Miguel Á S A

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Aug 13, 2019

Este curso es fantástico!!, he aprendido muchas cosas nuevas.

By Erik H

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Jul 18, 2019

Very insightful. I really enjoyed the programming assingments

By W X

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May 26, 2019

I have learned a lot from the course, and I had a lot of fun.

By Abraham K

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May 11, 2019

Loved it. All of the courses in the program are outstanding.

By Jose P

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Mar 22, 2019

This course, so far is surprisingly useful and well explained

By Graham M

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Feb 7, 2019

Very practical. Great to get to TensorFlow by the third week.

By poleNgrek

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Dec 9, 2018

A great course, suitable for everyone and easy to comprehend!

By Sidharth S

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Oct 22, 2018

Learned so much about deep learning optimisation. Amazing !!!

By Nwatu T J

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Mar 31, 2018

Concepts were presented with an intuitive approach. Awesome !

By Kurt S

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Mar 4, 2018

Good introduction and content for Optimizing Neural Networks.

By siddartha y

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Jan 22, 2018

Very fast. Would not recommend to non maths background people

By Kirsten S

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Nov 5, 2017

another amazing class. I really like the whole series so far.

By Muhammad A b C M

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Sep 15, 2017

Very good, such a new perspective obtained after this course.

By Gerzson D B

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Sep 4, 2017

Fantastic course, useful details, understandable explanation.

By Olorato M

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Aug 17, 2017

This is awesome but there are some confusing debugging issues

By Oliver O

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Aug 12, 2023

excellent course, the information is very clear and detailed

By Fabian I M N

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Mar 17, 2021

Awesome material and Andrew is one the best instructors ever

By Chris P

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Feb 18, 2021

Really good class. Definitely the right amount of challenge.

By Emilie C

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Sep 18, 2020

Great course, although we used an old version of TensorFlow.