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

4.9
stars
63,068 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

AA

Invalid date

Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course

AM

Invalid date

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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3076 - 3100 of 7,244 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Abraham K

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May 11, 2019

Loved it. All of the courses in the program are outstanding.

By Jose P

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Mar 22, 2019

This course, so far is surprisingly useful and well explained

By Graham M

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Feb 7, 2019

Very practical. Great to get to TensorFlow by the third week.

By poleNgrek

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Dec 9, 2018

A great course, suitable for everyone and easy to comprehend!

By Sidharth S

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Oct 22, 2018

Learned so much about deep learning optimisation. Amazing !!!

By Nwatu T J

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Mar 31, 2018

Concepts were presented with an intuitive approach. Awesome !

By Kurt S

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Mar 4, 2018

Good introduction and content for Optimizing Neural Networks.

By siddartha y

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Jan 22, 2018

Very fast. Would not recommend to non maths background people

By Kirsten S

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Nov 5, 2017

another amazing class. I really like the whole series so far.

By Muhammad A b C M

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Sep 15, 2017

Very good, such a new perspective obtained after this course.

By Gerzson D B

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Sep 4, 2017

Fantastic course, useful details, understandable explanation.

By Olorato M

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Aug 17, 2017

This is awesome but there are some confusing debugging issues

By Oliver O

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Aug 12, 2023

excellent course, the information is very clear and detailed

By Fabian I M N

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Mar 17, 2021

Awesome material and Andrew is one the best instructors ever

By Chris P

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Feb 18, 2021

Really good class. Definitely the right amount of challenge.

By Emilie C

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Sep 18, 2020

Great course, although we used an old version of TensorFlow.

By Kabakov B

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Aug 22, 2020

Great course, but please upgrade the final assignment to TF2

By Rishi G

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Aug 16, 2020

I finally understand how Adam optimizer works. Great course!

By Alfian R

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May 18, 2020

Importance notes on how to optimize the Deep Learning model.

By Kalyan M

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Apr 25, 2020

It helped me a lot to deeply understand the neural network

By David F

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Mar 25, 2020

Excellent course. Helper code still feels a little too much.

By Mariola N

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Feb 15, 2020

Excellent course and very useful introduction to Tensorflow.

By Saurabh M

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Feb 13, 2020

The course is awesome.....literally speaking... awesome...!!

By Ulugbek D

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Sep 7, 2019

Was very easy to follow. Straight-to-the-point instructions!

By Rahul G

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Jul 8, 2019

It helped me to gain deeper understanding of neural network.