Name: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Brand: DeepLearning.AI
Availability: OnlineOnly
Rating: 4.9 (7342 reviews)

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

4.9

stars

63,221 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

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

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.

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6951 - 6975 of 7,257 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Sam M

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Apr 28, 2018

Some errors in jupyter notebooks

By ccbttn

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Oct 8, 2017

last assignment need improvement

By Julian F

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Sep 30, 2017

A very practical hands-on study.

By San Z

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Jan 23, 2021

Tensorflow part is not that ok!

By Massimiliano L C

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Dec 19, 2019

Great course, incredibly useful

By Pavao S

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Feb 11, 2018

I would like to see more theory

By Saad K

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Sep 12, 2017

Could probably be more condense

By Yash A

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Nov 23, 2020