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Вернуться к Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Отзывы учащихся о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization от партнера deeplearning.ai

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Оценки: 60,803
Рецензии: 7,045

О курсе

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....

Лучшие рецензии

XG

30 окт. 2017 г.

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.

AM

8 окт. 2019 г.

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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301–325 из 6,996 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: Shabie I

18 февр. 2018 г.

Concepts buried deep in technical jargon and seemingly complex mathematical notation are laid out bare for everyone to understand.

Mr. Andrew Ng is a very special teacher. The humility and down-to-earth character also add immense value to the course. He makes you believe truly that you too can do it.

автор: Brandon E

26 сент. 2017 г.

An excellent continuation of the series. I particularly liked the in-depth discussion of Adam's optimization and the introduction to TensorFlow at the end of the course. The course does a great job of targeting specific concepts with practical advice related to tuning and optimization on real models.

автор: Kwan T

28 сент. 2017 г.

It is amazingly rewarding to learn from Andrew, who is able to articulate so much insights into so many complicated refinements of Deep Neural Networks from so many different research papers. The Tensorflow programming assignment is one of best tutorials I have seen. Thank you for your great effort.

автор: Olivera N

13 апр. 2021 г.

Really great experience taking this course! Truly diving in the area with many details. When I came to the last programming exercise with TensorFlow, I started to really appreciate the software frameworks that allow you to use predefined procedures instead of having to code everything from scratch.

автор: Zhiming C

18 апр. 2020 г.

A very good organize course! The knowledge is step by step introduced. From Python can one from scratch a learning code establish. And then the course turns into Tensorflow. Only with this method can man have good feeling about how Tensorflow is processed. Very good course, I strongly recommend it!

автор: Benjamín M

19 апр. 2020 г.

Concepts very well introduced and explained, with really good explanations about the intuition behind every topic. It's perfect to be able to apply different techniques knowing what they are good for and when to apply them, and at the same time it also shows where to delve deeper if needed/wanted.

автор: Nityesh A

8 окт. 2017 г.

Andrew Ng gives a good satisfactory explanation of the techniques covered in this course. He explains when to use the technique, how to use the technique and how one can implement it in Python and then goes on to give an intuition behind it. I think it should work well for newbies (worked for me).

автор: Tejaswini

25 мая 2020 г.

I've really enjoyed this course. It gives you a great deal of knowledge and I recommend this to anyone who wants to get an intuition of how to optimise, regularise and perform hyper parameter tuning to make your model learn efficiently. The variety of topics and depth offered was good. Thank you.

автор: Rujuta V

18 мая 2020 г.

This was an extremely informative course which provided an in-depth knowledge of how Hyper-parameters of Neural Network affect the results and methods of tuning those parameters from improving results. The Programming Assignment provides deeper insights of applying the taught methods effectively.

автор: Gary K N

5 мар. 2020 г.

This course adds to the first with what you need to make models perform well and fast in practice. Each part of the learning process has possible tuning, tweaks, optimizations to improve performance. The material explains why each tweak works, at least at an intuition level. I have learned a lot.

автор: Eddie C

18 февр. 2019 г.

My second AI course certificate from Andrew Ng after I left Taiwan AI Labs. Even though it took me more than 2 months to complete because of my kids' winter vacation and Chinese New Year break. I did learn a lot about how to tune and optimize a Deep Learning network. Keep going to the 3rd course.

автор: Shah M D

20 янв. 2019 г.

Great Course. This course does explain some optimisation algorithm with quit a good detail. That is a good part of it. Many less courses explain those algorithms at a level of abstraction an undergraduate student needs. Also, it shows the usage of tensorflow, which is used by major practitioners.

автор: Hoang T H

26 окт. 2018 г.

I think it's a great course for those who want to learn about technics related in Neural Network and don't want to know the mathmatical underlying too much, or for those who want to get an intuition or a picture about Neural Network. Thanks Dr. Ng and Coursera a lot for giving me a great course.

автор: Muthu R P E

2 февр. 2018 г.

Very good course. We learn the basics of Machine learning and Neural Networks in the earlier course. It works fine when we work with the examples given here, but in real world, our basic program does not work. The tuning process is more important for a successful model. Thanks to Prof Andrew Ng.

автор: Martin S

17 июня 2020 г.

Andrew Ng has a great teaching style, the lectures are always easy to follow and to the point. Weekly quizzes and programming exercises are very well done and help to reinforce the topics a lot.

The programming exercises in week 1 and 2 are very low-level and thus not relevant for real projects.

автор: Steven M

11 мар. 2018 г.

I felt like this course picked up specific problems and I was guided through them very well. Including theoretical aspects into the program assignments helped me to understand the concepts as I applied them! I also liked the funny comments every now and then. Great highly recommendable course!

автор: Gopikrishnan M

7 дек. 2017 г.

This is a beautiful course that builds on the first one, it gives all the intuitions about various hyperparameters and makes us implement all that in python. Then he when we start working with tensorflow, it all makes sense because we actually know what is going on in the functions that we use.

автор: Samuel Y

31 окт. 2019 г.

Incredible course. Very comprehensive, and goes over some awesome, industry-relevant optimization algorithms. Clear examples, programming assignments are extremely helpful, etc. Only things to improve would be to increase the difficulty of programming assignments, and focus more on Tensorflow.

автор: Andrei N

21 сент. 2019 г.

The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.

автор: Serkan K K

3 янв. 2022 г.

It has tremendously increased my intuition about hyperparameters. It made me fully grasp all the concepts. I would like to write more code in programming assignments, but if you follow the code that is already written carefully, it will be intuitive enough. Many thanks to Andrew and his team.

автор: Diego R

10 окт. 2020 г.

I really enjoyed this course. I think it is really well structured. The videos of Professor Andrew Ng are awesome and helpful (being able to explain very difficult issues in a nutshell with several examples and straightforward words!). I'm excited to go on with the rest of the specialization!

автор: Sanket D

25 мая 2020 г.

The course teaches many SOTA techniques for tuning hyperparameters, various regularization techniques, various optimization algorithms. Howerver, it would have been great to get a hands-on on hyperparameters tuning in real. Rest, the course is amazing and paves a smooth way to deep learning!!

автор: Mohammad S I ,

26 мар. 2020 г.

I am really glad to learn the tuning and optimization techniques. Hopefully, I can implement them whenever I need. Learning a new framework (TensorFlow) and using it to ease up the bigger calculations was the best thing about the course. Hats off to Andrew NG for designing a course like that.

автор: Mahmut K

29 нояб. 2018 г.

This second course was great in terms of showing improvements. I would have enjoyed a little more rigorous treatment of why improvements work, but then the course could go on and on... I sill think Andrew can spend a little more time on overcoming overfitting. All in all, excellent balance!

автор: Daniel D

19 авг. 2018 г.

It's a great course like the others and quite valuable. I am not quite sure how tensorflow fits into optimization, but I was glad to get a good, handholding kind of introduction to tensorflow as in these courses, I had become accustomed to doing things directly using numpy or MATLAB/Octave.