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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

YL

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very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

NC

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Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.

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1226 - 1250 of 7,238 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Yash B

Jul 11, 2020

This is hands down best course in coursera and i would suggest everyone in the electrinics engineering discipline to gettheir hands dirty on this.

By Gourav S

Jun 10, 2020

It has been a great journey and truly Andrew Ng is a great teacher. Most of my concepts are way clearer and I look forward to the next course now.

By Isara D S

Apr 9, 2020

It would be nice if there is more explanation on tensorflow framework, yet again you can never go wrong with the theories you learn in the course.

By heykel

Jan 24, 2020

Absolutely helpful for Newbies to Coding and Machine Learning.

It gives a great intuition also for managers, who are typically useless in coding...

By Anu S

Oct 22, 2019

It is a very good course . Covers most important parts of the hyper parameter tuning , very good explanation. I learnt a lot from the assignments.

By George Z

Jul 26, 2019

Another awesome course, covering optimization techniques, introducing us to Tensorflow and allowing us to explore numbers in the SIGNS datasets :)

By Mohammed A

Jun 1, 2019

Excellent Course that gave me a lot of insight and much of practical advice.

Highly Recommended.

Thanks, Andrew and the whole deeplearning.ai team.

By Sergej K

Sep 17, 2018

Insightful and good paced lectures. The notebooks are very well structured and very thoughtfully designed. Top notch course material plus lecture.

By Ashley C A

Apr 4, 2018

Excellent! Very good explanation of hyperparameters and possible optimizations.

Nice exercises, very illustrative. Learning Tensorflow was amazing!

By SzeYong P

Nov 16, 2017

The course materials are well presented and allows for progressive learning. The forum provides an excellent platforum for sharing and supporting.

By Desai A

Sep 23, 2017

Andrew Ng is the best. Course material is designed so nicely that even a beginner or an expert will also get interested to seek for what is next!!

By Rongcong X

Sep 18, 2017

Very helpful course to help me understand how to train my deep learning algorithms faster and gains better accuracy. Thanks a lot for this course!

By Aleksey I

Sep 9, 2017

Excellent self contained course! Andrew's approach to make you implement everything from scratch really pays off in better material understanding.

By Tyler D

May 14, 2023

wonderful uplifting course for each AI engineer, I strongly recommend this course for those who is willing to advance their deep learning skills.

By Altaf A

May 28, 2020

The course was quite nicely presented. If more links to the research papers could have been provided it would have been more informative and fun.

By Marc R

Apr 6, 2020

The course covers crucial techniques to ensure the training of deep NN is manageable, avoids all the pitfalls and results in adequate predictions

By 蕭博偉

Dec 12, 2019

Useful skills to improve/optimize deep learning performance, and provide a guidance to choice what to do next when facing bias/variance problems.

By Indranil B

May 19, 2019

This course did not just only open my abilities to deep learning framework's mechanics but also made me realize the use of those hyperparameters.

By Manpreet M

Jan 28, 2019

Good content and explanation. There are good practical suggestions given in the course. Also, TensorFlow programming is introduced which is nice!

By Horacio P

Jun 10, 2018

Amazing explanations. Great tips to solve and improve DNN problems. Exercises also are useful as base for robust projects. Thanks Andrew an Team!

By 杜凌波

Mar 6, 2018

Very fantastic lesson! Have learned much things by study it. someway,I think homework is too easy to review the knowledge.

Thanks you, Andrew Ng !

By Edgar V

Feb 12, 2018

With this course I not only understood more about DNN, but also learned to program better using Tensorflow in Jupyter notebooks. Thanks Andrew Ng

By Eduardo M O

Oct 8, 2017

Amazing intuitions and insights how to tune and understand "hyperparameters world". Thanks again Andrew Ng and Coursera for this amazing journey!

By Rami K

Aug 23, 2017

Excellent course, would have loved to learn more about Thiano in addition to TensoreFlow, but overall very happy with the content of this course.

By jyotikrishna B

Sep 17, 2021

really impressed by the way of teaching by Andrew sir, and got to know how to optimize the the neural network and improve its accuracy and speed