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

4.9

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5,375개의 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....

Apr 19, 2020

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

Jan 14, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

필터링 기준:

교육 기관: Shuvayan G D

•Jun 16, 2019

This is probably one of the best courses on hyperparameter tuning. Along with Andrew's teaching , the course assignments are just perfect to get the perfect intuition of how optimizers work in the deep learning frameworks , also you will be able to build your own optimizer from scratch after doing this course , though not recommended. : P

교육 기관: MOHD F

•May 17, 2019

This is an amazing course, it helps me a lot to gain the basic intuition, and the idea behind tunning our model, this course provides understanding basic maths of how we can knob various hyperparameters, which would eventually lead us to a better statistical model in term of both speed and performance... Thankyou coursera ...Thanks Andrew

교육 기관: Ivan T

•Feb 04, 2018

Another great course! Enjoyed it very much. Learned a lot of useful techniques. One thing that could be nice to do is to add references and optional material for students who want to go deeper (i.e. add references to publications related to some technique or a blog page). Looking forward to taking more courses in the Deep Learning series!

교육 기관: Jonah N

•Jun 04, 2018

The course really gave me insight into some of the optimization methods that are commonly used. It also helped me to get a better understanding of Tensorflow. I think y'all have done a good job presenting the information with just the right amount of math and explanation. I have recommended this course series to multiple friends already.

교육 기관: Arpit B

•Sep 11, 2017

Thanks Andrew, As always you have been a superb teacher, I am very happy with the content of the course.

One suggestion is to increase the level of difficulty in assignments. Or you can have one more course to develop an difficult deep learning application from scratch, through which we can all apply the concepts and tricks you explained.

교육 기관: Zihao Z

•Apr 25, 2020

It is really helpful to NN rookies like me. I have learnt a lot of important concepts and skills, such as hyperparameters tuning and variables initialization. More importantly, I gain some basic knowledge about Tensorflow, which is a widely used NN framework. I really appreciate the step-by-step instructions in the notebook assignments.

교육 기관: Yan

•Apr 13, 2019

Although the concepts of deep learning ( ie. the gradient descent, the chain rule ) are quite easy-understanding and clear to most people, how to choose the hyperparameter and how to effectively carry out the projects are real essence. That's what I learn from this course. Thanks for so many genius researchers contributing to this area.

교육 기관: Jayant R

•Feb 24, 2019

I didn't knew much about different optimization algorithms and how they work. This course helped in understanding those concepts. Also leaened how to tune hyperparameters. Now, I am able to read tensorflow codes on net and also able to write basic code. Prof. Andrew Ng is the best. Concepts gets very clear on first time watching video.

교육 기관: ALBERICO S D L F

•Apr 12, 2020

This is a best serie I've ever seen on digital courses overall, the sequence os topics are well planned and applied, the level is perfectly balanced to be challenger and also understandble. Contrags to professor Andrew and all his team for more on great resource to spread AI knowledge and make it accessible to most interested people.

교육 기관: Edwin G

•Dec 12, 2017

Some of the coding at the end was pretty tricky and I had to use the forums for help. That's what they're there for of course but I don't think the introduction to Tensorflow syntax was really sufficient - or maybe there could be some more optional help or resource to look through to help. Still very interesting and rewarding course!!

교육 기관: Virginia A

•Apr 07, 2020

highly illuminating. Finally, with this second course, I could grab the deep concepts and consequences of many terms I heard so many times during talks between data scientists. I feel now I could easily use what learnt to participate actively to those meetings and practically try things out on my methods and make them perform better.

교육 기관: Karan S

•Apr 27, 2019

I'd been working on Neural network Models in my undergrad projects, but really couldn't answer much of the problems that I faced. The title isn't too appealing, because no new Network Architectures are taught, but in my opinion, this course is on par with the previous course on building Deep Networks from scratch. Highly Recommended.

교육 기관: Anand K M

•Feb 10, 2018

A very nice course providing intuitions and concepts for tuning the hyper parameters in a neural network.

Also, provides a taste of using Tensor Flow (Neural Network Framework) in a comprehensive manner.

I would give my deepest thanks to the instructor, Prof. Andrew Ng for his invaluable time for building the course for the learners.

교육 기관: OMAL P B

•Feb 24, 2020

This is an wonderful course for people who want to learn about the ways to improve their models and make their best and more robust. Andrew Sir makes the math behind the scenes very easy to understand. The course is easy to follow as it gradually moves from the basics to more advanced topics, building gradually. Highly recommended.

교육 기관: Tanveer M

•Aug 28, 2019

Professor Ng's very clearly put a lot of thought into breaking down deep learning into the most understandable way for students around the world and it shows through the quality of this course. I cannot recommend this course enough to anybody who is looking to do machine learning, or simply understand the process from a high level.

교육 기관: Hari G S

•May 13, 2020

Excellent course over all, but what I like the most is how the complex math behind all these techniques are carefully hid and instead, we're given an intuition about how these techniques work. Of course, for a deeper understanding much more mathematics is needed, but they make sure that everyone has an idea about why things work.

교육 기관: Bernard O

•Oct 24, 2018

The tips and great guidelines one gets from this course are gems in their own right. Practitioners in particular will get to appreciate all the usable advice to improve their neural networks and at the same time get to understand the principles behind the scenes on what truly drives those optimizations. Highly recommended course.

교육 기관: Sinkovics K

•Aug 15, 2018

The course material is detailed and comprehensive and presented in a very digestible way. I felt like the home works lack a few topics (e.g. learning rate decay implementation, which I would find to be a useful exercise), but they give you a good understanding of what is going on. All in all, I definitely recommend this course!

교육 기관: Zhenwei Z

•Mar 16, 2020

After learning the basic knowledge of the first course, this course deepened my understanding of deep learning, and explained how to optimize the model of deep learning from the perspectives of hyperparameter tuning, regularization and normalization, and finally provided the basic knowledge of the framework based on TensorFlow.

교육 기관: Kazi M R

•Jun 06, 2018

From this course I have learnt several important hyper parameters, regularisation and optimization of deep neural network. Most importantly I got my first hand-on experience on Tensorflow framework by which creating deep net modes are quite easy if someone knows the elements of a deep net. I wish I will proceed for next course.

교육 기관: Ian C

•Oct 01, 2017

Learning about TensorFlow is brilliant. It's very hard to get a good understanding of what goes on in TensorFlow without fully understanding the neural network coding setup. This course beautifully combines the two. There were some minor frustrations with the final TensorFlow programming exercise, but overall this is excellent.

교육 기관: Jeroen M

•Jan 10, 2018

Great course, a few rough edges in the exercises and I also feel the exercise comments give away a bit too much (would be better if the student needed to figure out things by himself a little more). But these are minor details, I've learned a great deal in an amazingly short span of time, from one of the top minds in AI today!

교육 기관: Jeff R

•Oct 02, 2017

I appreciate the large amount of time that has gone into preparing this course. I note that there are a large number of corrections in the errata forum that have not been reviewed by staff. In particular there are some obvious errors in the programming assignments that could easily be corrected with a small investment in time.

교육 기관: Murat T

•Dec 24, 2018

Topics cut in to sections are well defined and so clear. Programming assignments definitely gives you hands on experience. Also, math is demystified that you track with high school math. If you used framework like Keras and you want to know why and when you need to use that function,parameter etc., you would love this course.

교육 기관: Gilles D

•Sep 06, 2017

Eventually a clear and definitive explanation about Network initialization, regularization and optimization. Good insight share on hyper-parameters prioritization.

We learn the how and why and suddenly, it all becomes a little bit less mysterious. It is all clearly explained in a very accessible way.

Great value for my needs