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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
62,856 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

AB

Aug 26, 2021

Amazing course which focus on the theoretical part of parameters tuning, but it needs more explanation of Tensorflow, as I felt a little lost in the last project. Except that, it is an amazing course.

NA

Jan 13, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

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6851 - 6875 of 7,218 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By erhan b

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Oct 20, 2017

Assignments are mostly copy past from instructions

By Agnes

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Oct 13, 2017

it is very useful for the processing of modelling.

By ZHE C

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Aug 17, 2017

include key idea on the tuning of hyperparameters

By Ahmed H

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Dec 19, 2022

multiclass classification need more illustration

By pranav b

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Jan 28, 2020

Best Course For Learning Hyper parameters Tuning

By 윤동준

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Mar 7, 2019

REALLY USEFUL.

BUT IT IS BIT HARD FOR BEGINNERS.

By Sumitabha B

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Jun 9, 2022

The last tutorial on Tensorflow could be better

By Shoaib Z

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Dec 29, 2020

1st and 2nd weeks are still somewhat confusing.

By bahri d

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Nov 25, 2020

Teorik anlatım harika ama uygulama kısmı eksik.

By JEROME D

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Sep 16, 2020

More than 10 videos is too much for just 1 quiz

By Nims F

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Jun 19, 2020

amazing course with an amazing teaching method.

By Santosh P

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Mar 22, 2020

Excellent course. Bit tougher than first course

By Kousik R

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Jun 12, 2019

There are so many grader problems please fix it

By Abhijith A

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Oct 1, 2018

Good course, could have done more on tensorflow

By abiran r

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Jul 12, 2020

i learn a lot of things related to tensorflow

By Mike R

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Nov 2, 2019

Tensor flow should be explained in more detail

By Kullawat C

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Oct 2, 2018

Very great course on how to tune NN in details

By Harsha S

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Jan 23, 2018

Builds on fundamentals, which is always good!!

By Tiến H N

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Apr 6, 2021

The coding assignment is not challenge enough

By Paraskevas P

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Mar 29, 2020

More practical examples would be very useful.

By Aymen S

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Aug 13, 2019

Cours intéressant merci beaucoup Mr Andrew Ng

By Anna

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Sep 12, 2018

batch optimization is good but not graded :-(

By Yiping W

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Jun 23, 2018

should provide more materials for tensor flow

By Abhishek A

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Nov 25, 2021

Tensor Flow could have been elaborated more.

By MD N F

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Feb 15, 2021

Concepts explanation was not up to the mark.