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

4.9
stars
63,219 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.

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

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7176 - 7200 of 7,257 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Rajat K S

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Jan 11, 2020

Most of the solutions to the assignment were written in instructions.

By Ganesan G

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Dec 28, 2017

I am not getting to see the programming exercises that i have done :(

By Adam S

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Oct 24, 2022

Some good stuff, but very slow, and the coding was pretty trivial.

By Jonghyun K

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Apr 25, 2020

voice was too small compared to noises made by clothes and others.

By Aastha S

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Jul 14, 2021

More explanations required for functions used in tensorflow lab

By FREDERIC T

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May 13, 2018

Good courses, the sound quality is very poor (high tone noise).

By Suhas M

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Jan 20, 2019

Interface for evaluating is not great and assignments are easy

By Alex

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Sep 4, 2017

The Tensorflow part should have started sooner in the course.

By Aloys N

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Jul 1, 2019

We could have more guidance on setting a tensorflow model

By HAMM,CHRISTOPHER A

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Apr 30, 2018

Lots of theory and not enough practical implementation.

By Stefan S

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Sep 22, 2020

Content starts to feel old, but still interesting.

By Hasnaa T

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Feb 10, 2020

the circulum was some hard and over detailed

By luca m

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May 5, 2020

I would have loved to have a session on TF2

By Kenneth C V

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Aug 29, 2019

Course is a bit complex due to the subject

By Kartheek

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Feb 1, 2019

week 3 topics would have been a bit better

By Tushar B

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Jun 12, 2018

Assignments vs lecture, difference is huge

By Aashita G

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Jun 1, 2020

fast paced not enough emphasis on topics

By Amod J

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Mar 18, 2018

Want to download my own work but cannot.

By Rachana O

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Aug 17, 2020

Can be done in more interesting manner.

By Mark L

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Jul 16, 2020

great superficial intro to the content

By jerome c

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Oct 14, 2018

Need more training on Tensorflow, imho

By Juan J D

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Sep 11, 2017

tensorflow subject was to superficial

By Weeha G

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Jul 25, 2021

Assignment of week 3 is toooo brief.

By SATHVIK S

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Jul 26, 2020

Can dive deeper into the mathematics

By Trevor M

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Nov 23, 2020

good lectures terrible exercises