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

4.9
stars
63,243 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

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.

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7201 - 7225 of 7,261 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Juan J D

•

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

By Maisam S W

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Oct 4, 2017

I still find tensorflow hard.

By Andrey L

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Oct 1, 2017

week 2 was extremely boring

By Cheran V

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May 9, 2020

Outdated with Tensorflow 1

By QUINTANA-AMATE, S

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Mar 11, 2018

Again, nice videos but not

By Matthew P

•

Sep 3, 2021

Focused a bit on minutia.

By Adam G

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Jul 11, 2020

Multiple grading issues.

By Chaitanya M

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Jul 1, 2020

could be more engaging

By José A G R

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May 23, 2023

Estoy muy emocionada

By Cory N

•

Jan 8, 2020

Update for TF2.0 :)

By Алексей А

•

Sep 7, 2017

Looks raw yet.

By Ilkhom

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Mar 21, 2019

awful sound

By Akhilesh

•

Mar 14, 2018

enjoyed :)

By Sai R

•

Nov 10, 2022

Good

By zhesihuang

•

Mar 3, 2019

good

By CARLOS G G

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Jul 14, 2018

good

By Hoàng N L

•

Feb 12, 2019

N/A

By KimSangsoo

•

Sep 17, 2018

괜찮음

By Dave L

•

Oct 3, 2024

The videos are good and go into a lot of the details. However, the programming tasks are not very useful. They basically expect you to plug certain lines into an almost complete programme, without really understanding what the individual lines - let alone the rest of the programme - really mean. Also, there is the occasional reference to lecture notes. However, those lecture notes are just the slides that are being used with hand-written annotations on top, and without the accompanying videos, they are not useful.

By Fabrizio N

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Dec 7, 2018

Good course content and clear exposition by Andrew. The course material however is not of a good standard. The slides can be downloaded but after all the hand scribbles by the tutor, they are barely decifrable. Some are just blank pages that need to be filled in with screenshots from of the videos. The assignements are often just a copy and paste exercise, and Jupyter crashes cause frequent loss of work.

By Goda R

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Feb 14, 2020

The video content is very good to get a good hang of theoretical aspects but the programming assignments are too spoon-fed because of which after doing filling the blanks, you don't feel confident enough to implement the same on your own. Instead the assignments should be changed to cases where instructions are given in words and entire function should be implemented by students.

By André Ø

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Nov 30, 2017

The TensorFlow part of the course felt out of place and not of the same quality as the previous material. It would have been better if another week was spent using TensorFlow to actually improving a NN and not just copy-paste an example into the assignment. Even after using TensorFlow in the assigment and passing, working with TensorFlow still