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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
38,127 ratings
4,052 reviews

About the Course

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization....

Top reviews

CV

Dec 24, 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\n\nThanks.

XG

Oct 31, 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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101 - 125 of 3,986 Reviews for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

By Pruthvi R R G

Jan 27, 2019

Superb Course for beginners!

By Tan T J

Jan 03, 2019

I found the content is very well organised and comprehensive. It truely is a good place to quickly build up the foundation as a Machine Learning Engineer.

By Hafize g g

Jan 03, 2019

Excellent course to understand how NN works without extensively using any deep learning frameworks (except tensorflow at the end)

By Ricardo A

Jan 04, 2019

Very practical tools to apply to training!

By Subham K

Jan 05, 2019

It was so awesome .I got to know the minute details which would certainly help me in making a better deep learning model.

By HARENDRA S

Jan 03, 2019

Very good course.

By Deepinder D

Jan 04, 2019

Awesome course!

By Jorge B

Jan 04, 2019

Very practical, clear and useful.

By GANHADHAR

Jan 04, 2019

Great Learning . Thanks a Lot

By Duy-Hung N

Jan 04, 2019

Thank you Andrew Ng you are my idol.

By pratik a

Jan 04, 2019

A great course!!

By Aravind D C

Jan 28, 2019

Very good course to understand the nuts and bolts behind the deep learning

By Antonio G C

Jan 29, 2019

It couldn't be better, thank you very much and greetings from Ensenada B.C. Mexico.

Very happy and hopeful that artificial intelligence can positively affect my country.

A hug.

By Manpreet M

Jan 29, 2019

Good content and explanation. There are good practical suggestions given in the course. Also, TensorFlow programming is introduced which is nice!

By Saeed A J A

Jan 29, 2019

Thanks for your guidance

By Rob

Jan 30, 2019

Really enjoyed the last section on TensorFlow

By Dan L

Jan 30, 2019

great course - program assignments can be a bit harder... plus, maybe more tensorflow assignments can be really useful

By Rahul K

Jan 28, 2019

very good content on Hyperparameter tuning , regularization ,Optimization and tensorflow

By Patrick M

Jan 29, 2019

Thank you so much for this course. It's been really insightful and helpful

By 王新越

Jan 29, 2019

The course is useful for me ,and I have learned much.

By Bạch T T

Feb 01, 2019

It's great to know how to adjust hyperparameters and make my NN work more efficiently

By Raymond T Q T

Jan 31, 2019

very good lectures

By 任华琛

Feb 02, 2019

very helpful

By Andreea A

Feb 02, 2019

This was a useful course for newbies in neural networks. It gave useful hints regarding how to update the model one is using based on what problems one observes, as well as how to tune the hyperparameters (if there is enough computational power or one runs a small problem). Obviously, this is just a starting point and one should invest a lot of time and energy to become experienced.

By Chandrakant P

Feb 02, 2019

Thank you Andrew Ng