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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
63,484 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

RR

Jun 13, 2020

Could have increased assignments and some more indepth knowledge of tensorflow and proper installation way of tensorflow cause mine is showing error when iam trying to practice as shown in the video

NC

Jun 3, 2018

Just as great as the previous course. I feel like I have a much better chance at figuring out what to do to improve the performance of a neural network and TensorFlow makes much more sense to me now.

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5926 - 5950 of 7,283 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Dave P

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Oct 11, 2017

Hello

By Shiyuan Z

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Oct 3, 2017

good!

By 刘其波

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Oct 2, 2017

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By Rafael L

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Sep 27, 2017

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By Hong W

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Sep 25, 2017

Sweet

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Aug 25, 2017

12/10

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Jun 30, 2024

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Sep 18, 2023

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Apr 8, 2022

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Mar 11, 2022

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Jan 12, 2022

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Sep 5, 2021

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Aug 14, 2021

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Aug 7, 2021

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Jul 7, 2021

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Jun 28, 2021

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Apr 22, 2021

Nice

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Feb 18, 2021

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Dec 17, 2020

nice

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Dec 11, 2020

nice

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Oct 16, 2020

Best

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Sep 30, 2020

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Sep 29, 2020

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Sep 28, 2020

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Sep 19, 2020

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