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

4.9
stars
62,800 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

JS

Apr 4, 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

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

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176 - 200 of 7,215 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Tran C

Sep 11, 2022

Very useful and detailed !! And also let's not forget the things I love most about this series , Andrew's dedication in explaining new concept and code , and the design of the practice assignment ( the coding lab ) . The lab is very very helpful !! The non-relevant code are already typed in by the author , so that we can focus on the main code that supports us in understanding the new concept

By Mark R

Mar 22, 2021

Another excellent course. It provides a good background for understanding more about neural networks with a reasonable amount of time and effort. I have no illusion that it is providing knowledge in depth, but I have a much better knowledge of the basic terms and concepts that I did before. I am pleased to know at least something about tensorflow and how to use it to build neural networks.

By Mukund C

Oct 14, 2019

Excellent Course. Really structured way of learning the importance of hyper parameters and their effects on the learning/training and hammering concepts like "regularization" home.

Just an observations, but it seems like the mentors are not that engaged in these courses anymore, but there are enough help threads that one can figure out the questions - specifically on the programming exercises.

By Ayush K

Jun 16, 2018

What an amazing course it is. Perfect explanation how we can use optimize our cost more efficiently and effectively. Also this course includes techniques to overcome problems like over fitting i.e Regularization and Dropout techniques. Information about Batch Normalization is very splendid. Also got little intuition about tensor flow. Thank You Andrew Ng for providing such a wonderful course.

By colinyu

Jan 15, 2018

Prof Ng is a great teacher and is good at making the difficult material very easy to learn. I am very interested in the DL. Before I took this class, I found that since this field is very new so all the material you can find is a little piece and not systematical. This specialization is a wonderful and systematical, easy to learn and fun. Thanks for the great work those teacher have done .

By shengtian z

Mar 8, 2018

Awesome illustration on deep network's regularization techniques, weight initialization techniques and gradient checking, and more. This class provides you with hands-on experience with how to tune a deep network efficiently. You will not only learn the techniques but also understand many of the intuitions of how each technique works. A must take if you are dedicated into machine learning!

By Patricio G

Oct 15, 2021

Comencé esta especialización sin conocimientos de deeplearning en absoluto, hoy habiendo finalizado la especialización tengo una basta noción de este mundo tan apasionante. Quiero destacar la facilidad con la que Andrew transmite su conocimiento, es un instructor de otro mundo!. Feliz de haber realizado la especialización y de continuar por este camino. Gracias a Andrew Ng. y a Coursera.

By Rahul B

Sep 5, 2020

This has been a very useful course and helps you to understand much more about neural networks including regularization, optimization algorithms, hyper parameter tuning and programming frameworks. The style of teaching and the programming assignments are of a really good standard. The quizes could be improved to be a bit more challenging but they still help to review content quite well.

By Rusty M

Dec 7, 2018

I learned a lot about the area that is not much talked about in deep learning, which is hyperparameter tuning! The forum was very helpful in debugging the programming assignments! Thank you Prof. Ng for the wonderful course. I thank Coursera as well for believing in me and granting me Financial Aid. It wouldn't have been possible without your help, Coursera Team. THANK YOU VERY MUCH! :D

By Neeraj B

Oct 2, 2019

This was an excellent follow-up of the first course. Having used adam optimization for almost all the neural network models I have build it was great to understand the mathematical intuition behind adam optimizers. Also the programming assignment gave a wonderful refresher and practice of tensorflow. Overall I'm glad hyperparameter tuning and optimization was chosen as a seperate course

By Manraj S C

Oct 16, 2019

The course is great and will help you in understanding on how to optimize your deep learning algorithm and tune your hyper-parameters. The course provides insights into the exponentially weighted averages concept too which helps you understand how things work behind the scenes when trying to optimize your algorithm. Dropout and regularization have also been explained to a good extent.

By Chan-Se-Yeun

May 1, 2018

This course is very useful for practical purpose. I've learnt a systematic method to develop and iterate my algorithms, which saves me a lot of time. And it's been the first time that I get to know so many variants of gradient descent method, such as Adam and RMSprop. By the way, the programming assignments get a bit hard, but it help me better understand the algorithms. Thanks a lot!

By Andreea A

Feb 1, 2019

This was a useful course for newbies in neural networks. It gave useful hints regarding how to update the model one is using based on what problems one observes, as well as how to tune the hyperparameters (if there is enough computational power or one runs a small problem). Obviously, this is just a starting point and one should invest a lot of time and energy to become experienced.

By Jay G

Sep 23, 2018

All the quality of the first course, but even better. My 4-stars for course one were addressed in these Jupyter notebooks. They were still manageable but the prompts provided very good reinforcement to the various tuning algorithms. A top-notch offering...one I'll be sure to recommend broadly. I'm very much looking forward to the remaining courses in the Specialization. Thanks!

By Sarthak K

Aug 12, 2019

I had a very good time getting teaching sessions from ANDREW NG .., I am a second year student and have entered in this field of deep learning since some months then i encountered this specialization and with the deep concepts of Sir ANDREW NG ,i am now able to make much more complicated models ever before...I hope i could get an autograph from my Ideal in this field

Mr.Andrew Ng

By sujith

Oct 26, 2018

This is a great course to learn about practical aspects of neural networks. Some parts are challenging to consume as most of the material relies on intuition rather than detailed mathematical explanation. This helps to involve more people in the course who are intimidated by mathematical equations. A great addition would be to have optional mathematical details in separate videos.

By Shangjin T

Mar 2, 2018

I've learnt much from course including preprocessing (mini-batch, regularization, normalization), gradient descent algorithm (batch gradient descent, stochastic gradient descent, mini-batch gradient descent) and the variants (momentum, RMSProp, Adam). Also there's TensorFlow tutorials which I love best.

Thanks for Andrew Ng for bringing us such an amazing fundamental course of DNN!

By Aakash K S

Oct 30, 2020

Very well structured and thorough course. Instructor did a very good job in teaching the topics of NN such as Regularization, Optimization etc. and explaining the mathematical concepts such as moving average.

Lab made coding assignments easy to understand and code. Lab made it easy for me to understand the structure of NN and how to code clean NN functions for easy implementation.

By Adam D

Mar 21, 2021

Excellent! This was number two in the series and both courses so far have been excellent. That notation and frameworks are consistent and build nicely. The exercises, quizes and programming assignments really reinforce the material.

This course is true to its title and one you should definitely take if you want to understand a lot of the nuances of constructing a deep neural net.

By Sourabh G

Oct 17, 2019

This course really helped me getting the deep insight into the hyper-parameters which need to be tuned to get the optimal learning of the algorithm with the different algorithms necessary for improving learning rate.Andrew Ng really simplified the tough things and arranged them in a proper series of videos that is easy to understand.This will really help me lot in future.Cheers!

By Christian H

Feb 7, 2021

Great course, but please have the TA proofread the subtitles (the auto-generated ones are bad, if I was deaf, I would have no chance to understand anything) and upgrade to at least 1080p video. After all, since this course costs tuition, there should be a minimum technical standard guaranteed.

But content wise and teaching style wise this is fantastic, thank you so much, Andrew!

By Danilo Đ

Dec 3, 2017

I suppose Hyperparameter tuning, Regularization and Optimization are some of the most important aspects of Deep Learning, since 90% of most of the DL projects come down to just that. Andrew masterfully dives into the intuitions behind some of the most widely used approaches, and the programing assignments are designed to show the impact good tuning could have on a DL algorithm.

By mohammed a

Jan 7, 2018

Great explanation of optimizations that can help speed up deep learning algorithms. Loved the little tips and tricks that are covered in different sections. The easy with which Prof. Ng explains complex concepts and analogies is commendable. The programming assignments are very helpful to people without expert programming experience too, that makes the experience very smooth.

By Francisco G A

Oct 24, 2020

An Excelent course on learning how to tune hyperparameters and also a peek in using TensorFlow (in the last week, and trust me, it's worth the wating) it's complete, educational and the professor Andrew Ng it's an excelent teacher! I would have liked some more practice on parameter tuning in the programming excercises. But in a nutshell it was a awesome learning experience!

By Anirudh S

Nov 6, 2017

In my opinion it would be a good to have a short video describing how to drive the ml project in the company. As i am taking ml course and this specialization, I started with working on octave, then numpy then tensorflow, so it would be good to have some advice/tips on when to use octave or numpy or tensorflow for building a model when you get a project in ml in your job.