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

4.9
stars
63,175 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

XG

Oct 30, 2017

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

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1201 - 1225 of 7,253 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Md. R H

May 9, 2018

This course is very important for deep learning parameter tuning and internal architecture. From this course i learn how to tune the hyper parameter.

By 孟方良子

Mar 22, 2018

This course is wonderful and impressing. I have learned a lot in this course. However, I think I have not got the ability to use tensorflow expertly.

By Bharatram

Dec 30, 2017

Nice introduction to hyper-parameter tuning and tensor flow. This will give better insight on development of neural network with better understanding

By yuji w

Oct 4, 2017

It is great to extend my knowledge on deep learning, neural networks. It is also nice to touch on to see a bit of tensorflow aspect of deep learning.

By Eric B

Mar 3, 2021

Professor Ng provides a good overview of the various hyperparameters and guidance from experience on how to proceed with their selection and tuning.

By Aadith K

Oct 4, 2019

This course is a natural continuation to the first course in the Deep Learning specialization! I had a lot of fun doing the programming assignments.

By Abdirahman A

Sep 20, 2019

Short course, but a good introduction to the topic. To get a good understanding of certain topics however I felt its necessary to some extra reading

By 王浩礴

Jun 3, 2019

a good overview of some of the hyperparameters tuning in deep learning that normally will not tell in class. Also introduce the basics of tensorflow

By saurabh k

May 8, 2019

As Always I am highly convinced by the teaching and course content. It is always a pleasure to learn from Andrew Ng himself and make best out of it.

By Emmanuel A

Jan 13, 2019

Andrew Ng gives practical tips to improve a deep neural network but also the theory behind. I highly recommend this course for every ML enthusiasts!

By Caner Ö

Aug 30, 2018

I didn't know about the importance of gradient checking and some practical uses of TF, before I was beginning this course. Thank you deeplearning.ai

By Chris Y

Dec 26, 2020

Great content. Overall the homework is not challenging but serves the purpose well to help you understand thought the knowledge taught in lectures.

By 17DIT018 N K

Jul 27, 2020

This course helps a lot in learning how to optimise the algorithm and how to deal with both training and testing accuracy and apply it practically.

By Helmuth T

May 27, 2020

Excellent course. As always, Andrew Ng taks a set of complex concepts and explains them brilliantly, making them easy to understand. He is amazing!

By Kaivalya B

May 8, 2020

The explanation of maths could've been better in reading notes or something after video. Overall as usual brilliantly explained by Prof. Andrew Ng.

By Neelesh A

Jan 31, 2020

Absolutely elegantly done by Prof. Andrew Ng. Have become a Deep Learning addict, thanks to the professor. Eagerly looking forward to the next one!

By Mokshda S

Jun 21, 2019

Really helpful course .I was provided with proper guidance and I would recommend this course for improving knowledge in the field of deep learning.

By Arun K M

Apr 3, 2019

The course modules were very well paced, with details as well as the programming of specific aspects of deep learning clearly explained. Thank you!

By Ravi S K

Oct 3, 2018

Prof. Andrew is an excellent teacher. His lectures engage the students to keep going; he also encourages the students to complete their coursework.

By Ashwin G D

Sep 25, 2018

A very good course to learn optimization in Deep Neural Networks. It provides the knowledge in both, broader dimension as well as deeper dimension.

By Viktor P

Apr 6, 2018

A very well-structured and entertaining course. Programming assignments are prepared well, so you don't have to spend a lot of time with debugging.

By Tinsae G A

Feb 10, 2018

It was challenging; more math, more algorithms, and tougher assignments were involved. I liked it anyways; because challenge creates more learning.

By Deleted A

Feb 8, 2018

Great lectures as usual, in style and coverage. Quite a bit of hand-holding in programming exercises, which cuts both ways (positive and negative).

By Shardul L

Dec 9, 2017

Got a hands on experience with Tensorflow. Must learn course. All the pieces start falling together when you try Tensorflow programming assignment.

By Rizwan M

Nov 21, 2017

A very important course for developing efficient deep learning models. Andrew's explanations made it really simple to grasp these complex concepts.