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

4.9
stars
63,523 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

DH

Apr 26, 2020

Everything, Everyparameter in neural networks looks familiar to me now. I feel like I can optimize them for better accuracy. Overall I learned some new things and the way of teaching was really nice.

DD

Mar 28, 2020

I have done two courses under Andrew ng and I am grateful to Coursera for their highly optimised and easily learning course structure. It has greatly help me gain confidence in this field. Thank you.

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6851 - 6875 of 7,292 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Duberney L R

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Jun 13, 2022

Deben mejorar las presentaciones y la traducción al español

By Joao V

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Nov 17, 2020

I hope I get to learn more about TF in the upcoming courses

By Suyash J

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Jun 11, 2020

Very Good Course, best if include notes for quick revision.

By Vishakha S

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Feb 5, 2020

I think a short video on tensorflow might help the learners

By Ernst H

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Jul 7, 2019

Obvious problems. Lessons and quizzes need to be polished.

By Nick R

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Jan 7, 2018

Necessary background information and how-to for algorithms.

By Nimish P

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Dec 9, 2021

Great explanations. A few more assignments could be given.

By Deleted A

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Jun 16, 2020

Loved the course, content and assignments are really good.

By Disheng M

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Mar 25, 2020

Tensorflow is kind of outdated, hard to find documentation

By 熊子量

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Feb 10, 2020

Less theoretical but more practical than the first course!

By Ashraf A

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Oct 26, 2019

Very good course and a good introduction to the tensorflow

By Yating G

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Jul 14, 2019

The courses are vey well organized and easy to understand.

By Khosro ( P

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Mar 22, 2019

Great course but homework assignments are a bit confusing!

By zhixing x

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Sep 8, 2018

Some mistakes in the notes, but overall it's a good course

By Timothy D

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Oct 2, 2017

This was a great course. Batch Norm blew my mind. Thanks

By thiên t

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Jan 1, 2021

I can't use tensorflow v1 on my computer and Google Colab

By Karthi C

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Jun 26, 2018

Became hard core technical but that's what it mean to be.

By ภาณุเทพ ท

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Jun 26, 2018

So far so good, but why no batch norm in last assignment.

By Luis M A

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Apr 4, 2018

As the 1st course, really easy to follow and interesting!

By Evaristo C

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Sep 17, 2017

Something that you won't see in many other MOOCs, worthy!

By Rafael M

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Aug 31, 2017

Very good.

Would be better it it touches tools like keras.

By 方嘉浩

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Jun 7, 2022

improve my understand about Deep Neural Networks to work

By hatem a a

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Sep 13, 2021

Very goog course,but the code section need more explain

By Nilange o s

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Jul 3, 2020

Good course , 'll get to know more about hyperparameters

By Le K H P

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Dec 20, 2017

A bit short on the Tensorflow tutorial, otherwise great.