Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization(으)로 돌아가기

4.9

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5,431개의 리뷰

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.
After 3 weeks, you will:
- Understand industry best-practices for building deep learning applications.
- Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
- Be able to implement a neural network in TensorFlow.
This is the second course of the Deep Learning Specialization....

Oct 09, 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

Oct 31, 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.

필터링 기준:

교육 기관: Vincenzo M

•Sep 11, 2017

This course will becoma a foundamental course for people that aim to work in the machine learning / deep learning area because it presents clearly the recent innovations in the deep learning. For production environment people will probably use open source framework, but this course clarify what is behind.

교육 기관: JOSHY J

•Oct 03, 2019

Excellent course if you are passioned about Deep Learning. Walk you through the most basics on how to tune the model parameters so that you can reach the highest accuracy for the model. The lecture is simple and well ordered. The TensorFlow introduction part is more exciting. Overall a wonderful course.

교육 기관: Dimitrios L

•Feb 18, 2018

Excellent course! Not only does it address critical deep-NNs training issues providing a clear exaplanation around why these tunings are needed, but also provides some empirical advices (e.g. on level of importance on the hyper-parameters, typical values etc) that can be valuable when training depp NNs.

교육 기관: Aaron B

•Oct 28, 2018

The only thing I wish for is a 'live chat' when an instructor is available, a IRC/slack/chat room for students to help each other, or faster response time when posting to the forums. Also the forums are a bit clunky (I don't remember all the reasons why), but the search allowed me to find useful posts.

교육 기관: Shashank M

•Oct 10, 2018

This course offers a very quick introduction to methods that could be used to improve usage of deep nets from a practitioner's perspective. Although the mathematical details are not covered in depth, the material furnishes concise list of topics that could be researched upon for in-depth understanding.

교육 기관: Sachin G W

•Dec 11, 2018

Amazing course, starts right off the bat with hyperparameters, regularization and tunings.

Studied about various optimization algorithms and normalization alongwith mini batches, also the TensorFlow framework.

Thank you to everyone involved in making this course. I highly appreciate what you've made us.

교육 기관: Muhammad s k

•Dec 03, 2019

I always held an opinion that highly qualified instructors, specifically those holding doctorate degrees are not the good teachers because they can't teach students at their levels. But Sir Andrew Ng proved me wrong, he is a wonderful teacher and tries to explain the minute details.

Salute to you sir.

교육 기관: Edoardo S

•Jan 20, 2019

Very impressive course, really well done and interesting. One suggestion: apart from the modelling part in the programming assignment, I would also introduce some coding about the computing of the results and the final cost plot (in all the programming assignment these parts are already pre-compiled)

교육 기관: Shabie I

•Feb 18, 2018

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.

교육 기관: Brandon E

•Sep 26, 2017

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.

교육 기관: Kwan T

•Sep 28, 2017

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.

교육 기관: Zhiming C

•Apr 18, 2020

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!

교육 기관: Benjamín M

•Apr 19, 2020

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.

교육 기관: Nityesh A

•Oct 08, 2017

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).

교육 기관: Tejaswini

•May 25, 2020

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.

교육 기관: Rujuta V

•May 18, 2020

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.

교육 기관: Gary N

•Mar 05, 2020

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.

교육 기관: Eddie C

•Feb 18, 2019

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.

교육 기관: Shah M D

•Jan 20, 2019

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.

교육 기관: Hoang T H

•Oct 26, 2018

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.

교육 기관: Muthu R P E

•Feb 03, 2018

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.

교육 기관: Steven M

•Mar 11, 2018

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!

교육 기관: Gopikrishnan M

•Dec 07, 2017

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.

교육 기관: Samuel Y

•Oct 31, 2019

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.

교육 기관: Andrei N

•Sep 21, 2019

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.