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

4.9
stars
63,243 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

AM

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

CM

Dec 23, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow

Thanks.

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3051 - 3075 of 7,261 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By rachit g

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Jul 17, 2018

Excellent course. This course has no substitute in the market.

By Ganesh R

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Jun 30, 2018

Good course to understand the Hyperparameters and Optimization

By Divya R

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Mar 31, 2018

Great, but gets complex. Which is good, but also difficult! :)

By Cheryl A

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Mar 21, 2018

Really great insights and intuition on tuning and optimization

By Phi N V

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Dec 14, 2017

the course is so good but the problem set should be more handy

By Amit P

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Dec 7, 2017

Anyone who wants to learn Deep learning must take this course.

By Rodney R

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Nov 16, 2017

Amazing course! That explain step by step how to do.

Thank you.

By Amy L

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Oct 24, 2017

Amazing Course, the teaching approach is from shallow to deep.

By Khurram S

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Oct 13, 2017

Great course! good to learn Hyper Parameter to tune Parameter.

By Sarth S

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Feb 15, 2024

Excellent course for finer details of machine learning models

By Milad S

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Oct 29, 2021

this is LOVELY course, TNX you mr NG and coursera.

with LOVE

By Shahnawaz S

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Sep 14, 2021

I like the content and the way trainer explained all about it

By Hengrui C

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Jul 22, 2021

Great introduction to tensorflow and related tuning concepts.

By Alejandro F N

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Mar 6, 2021

Great continuation for improving my neural networks knowledge

By Saurabh S

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Feb 23, 2021

Very much enjoyed doing this course. Cheers, Andrew and team.

By Apurva T

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Nov 28, 2020

Perfect course to learn Hyper-parameter tuning and Tensorflow

By Rafael M

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Jul 23, 2020

Excellent course. Only downside is week3 is in TensorFlow 1.0

By Anil R

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Jul 3, 2020

Awesome course, Thanks Andrew ng to taught again great topics

By haiderali

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Jun 3, 2020

Simple, fact full and amazing course. Definitely recommended.

By Vishal S

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May 15, 2020

Very good course for Deep Learning revision before interviews

By Siba S P

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May 12, 2020

Yet another wonderful course by deeplearning.ai and Andrew Ng

By vikash c m

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Jan 2, 2020

Best course for Hyperparameter tuning of Deep neural network.

By Douglas C

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Dec 28, 2019

Well structured. Maybe too much help on the coding exercizes?

By Amish T

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Dec 18, 2019

Excellent course. It helps in fine tuning your neural neteork

By 曾湘

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Nov 25, 2019

nice design to clearly show the principle of neural networks.