Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization に戻る

4.9

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52,331件の評価

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5,916件のレビュー

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....

Dec 24, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

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.

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by Nimish S

•Aug 15, 2017

Having done multiple Udacity Nano Degrees and other deep-learning/AI courses on Coursera/edX, I can say that deeplearning specialization is probably the best and most detailed to master the basics of Neural Networks and deep learning. This course is great in helping understand tuning of hyper-params, various optimization techniques and approaches. Videos do a great job in explaining complex and confusing concepts in easy to understand style. Assignments cement the understanding further.

Kudos to the Prof Andres Ng and rest of the deeplearning.ai team for putting up such a great content.

by Shahin A

•Mar 08, 2020

It is very important for students to feel that the instructor and the education system are in their side, not in the confronting side. I feel like the whole deeplearning.ai course, from design elements to teaching, are in my side, they are here to help me learn. It is great!

The negative side is using TensorFlow1. The python package is clearly an interface to a lower level language, and thus either some background in that language is needed to understand the process better, or it is better to migrate to TensorFlow2. Why TF1 when version 2 is available and it is much easier?

by Ali Z

•Nov 01, 2018

small description error on the last project. tensorflow tutorial project.

X, Y = create_placeholders(12288, 6)

print ("X = " + str(X))

print ("Y = " + str(Y))

X = Tensor("Placeholder:0", shape=(12288, ?), dtype=float32)

Y = Tensor("Placeholder_1:0", shape=(6, ?), dtype=float32)

Expected Output:

X Tensor("Placeholder_1:0", shape=(12288, ?), dtype=float32) (not necessarily Placeholder_1)

Correct this from Y Tensor("Placeholder_2:0", shape=(10, ?), dtype=float32) (not necessarily Placeholder_2)

to:

Tensor("Placeholder_2:0", shape=(6 ?), dtype=float32) (not necessarily Placeholder_2)

by Shahed B S

•May 31, 2018

This course goes into the various parameters and hyperparameters of deep neural networks, as well as suggestive values for ones we can use. This course is short in duration, but a lot of content is developed in here. It touches in on Tensorflow. The template based assignments provide great intuition for getting right on to the topics being taught, however, I feel there should be scope for more programming assignments where the student should be able to write more of that template as well. All in all, Andrew Ng is a great teacher and it was a pleasure to learn from him.

by Jong H S

•Oct 02, 2017

At the time of writing this review, I have completed 3 of the 5 courses. I personally think these 3 courses are not merely courses to fill up the specialization. It is a journey, an incredible one. I will write metaphorically. My journey so far is like becoming a magician with Course 1 on how to become one, then went on to Course 2 to learn from the master magicians, their secrets revealed and Course 3 on what to do to put up a good show at Las Vegas trying to fool Penn and Teller. This specialization is my treasure vault. Great job to Prof Andrew Ng and team.

by Vincent F

•Jan 23, 2018

This course provided me with an understanding of the large number of hyper parameters that have to be tuned during a deep learning project. It gave me an insight on when different techniques like regularization and (the many different forms of) optimization need to be applied. The only quibble I have is that the material on the choice of the number of layers and the number of hidden units per layer was thin. Given that these values have a great impact on the speed of progress in a deep learning project I would have liked to have seen a little more emphasis on them.

by Ernest S

•Nov 05, 2017

This course offers ground knowledge in all mayor concepts of non-recursive neural network and is excellent preparation to further exploring of this topic. Lectures cover broad choice of topics and discusses many problems you might encounter during your journey. Professor Andrew Ng explains theory in a way which builds good intuition and gives you building blocks for face the challenges of machine learning. If you are fluent with calculus or have academic background and expect to discover math behind the scenes I think you will be content too. I surely was.

by Aditya B

•Jan 12, 2019

The concepts has been explained in a fantastic way. But few suggestions:

-> After every lesson, I would love to have more pop quizes. This was the case with course 1, but I didnot get any pop quizes for this one.

-> In the quiz assignment, it would be nice to have an explanation or justification section, which will explain that why the option selected is a correct one and why the other options are incorrect. I know we can have the same discussion in the forums, but such an explanation ( one liner should be fine) can provide a good instant knowledge boost!

by Robin S

•Jun 09, 2018

Another very well done course. You do a good job describing the benefits of Batch Norm, a lot more intuitively than presented in Szegedy's paper, which is pretty math heavy. However, I did notice one little ERROR on the Tensorflow project page, albeit an insignificant one. Double check that the expected output shape for the cell that outputs the shape of the training set and testing set. One of the expected outputs said that the test set should have 10 possible classes, when the dataset is for 0-5 fingers. This would be a very strange looking hand ;)

by Mohanad Q A A

•Feb 26, 2019

I'm actually learning and comprehending the course, I do pause the videos occasionally to research some concepts, write some notes in a copybook but overall this specialty(so far course 1 & 2 ) is really filling the gaps in my mind to build a clearer picture of the topic of Machine Learning and Deep Learning. Andrew Ng explains really well, sometimes he through some good recommendations based in his practical experience and this is really valuable for me because it actually helps in improving the learning process.

Thank you Andrew and Coursera Team.

by Yashveer S Y

•Jun 02, 2018

This course is perfect bite for your hunger of Deep learning. Before taking this course I have gone through some books and and some blogs too but there was not that much of clarity to topic so finally I tried for this one and trust me this course is so organised and very informatic so go for this one, I assure you will feel more confident and knowledgeable after completing this course. I would like to thanks Coursera as well as deeplearning.ai community for providing this course and Want to specially thanks to Mr. Andrew Ng for his contribution

by David M

•Sep 01, 2017

This is a practical course on how to work with neural networks. It covers a collection of "tips" and techniques, all grounded on a solid theoretical framework, to make a classifier train faster and be more accurate. The explanations are all engaging and interesting, and the assignments are rather easy.

The knowledge gained from this course is probably what everybody working in machine learning already knows, but if you are new to the field this is a great way to get up to speed fast and start implementing neural networks for your own projects.

by Jairo J P H

•Feb 01, 2020

El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especialización en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias…!

The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!

by Youdinghuan C

•Dec 29, 2017

This is a logical continuation of the previous course. The 3-week topics were excellently chosen. Andrew did a great job of delivering the lectures. The programming assignments really reinforced my understanding. In particular, essential knowledge and equations from video lectures were reiterated in the programming assignments for review and ease of reference. The amount of work was reasonable, and the level of challenge was appropriate. I especially appreciate the instructional team for making this course open to the public.

by Alessandro T

•Jan 22, 2018

A right balance between theory (you are required to code know the models and code them from scratch) and practice (you get an overview of the frameworks available out there to put your code into production quickly and efficiently; and time is spent on practical aspects of the training phase).

A small "criticism": in the notebook, more than programming you just have to fill a template where a good part of the algorithm is already drafted for you. It is too much, students should be left scratching their heads a bit longer :)

by Heshmat S

•Dec 27, 2017

This is the 2nd course from Andrew Ng in the "deep learning specialization". Having introduced the building blocks of deep neural networks, in this course Andrew teaches more advanced and practical concepts - like: regularization, advanced optimization techniques, batch-normalization, etc - that can significantly improve the implementation of the models we build.

Also, in this course we get to learn TensorFlow, a widely used and wonderful deep learning framework.

I highly recommend this course.

Thank you Andrew & Co. :-)

by Beng C C

•Dec 31, 2018

Great course! But I am not too sure why this should be placed in number 2, as I feel that topics such as tuning hyperparameters do not resonate well with someone who is not working professionally or is not very experienced in this field. However, still a great course as I will revisit this course when I gain more experience. I also like the last exercise on Tensorflow as there is a lack of courses on Tensorflow on the Internet, so the last assignment on Tensorflow is the most useful which I have found in the course.

by Nkululeko N

•Apr 12, 2020

Other than anything I've learned a great intuition about everything that Andrew Ng has presented in this course. Some I somewhat still feel like I still need to do some further readings and understanding because some of the concept from the course I still don't understand them. However, with the first course and this course of the deep learning specialization, I feel ready to work as a machine learning expert even if starting from bottom-up. I feel more than ready to finish the whole specialization certificate.

by Hassan S

•Apr 03, 2018

Andrew Ng and the teaching assistants' team of this class are obviously very very determined not to leave any single major subject in deep learning without coverage. I have been using deep learning for the past couple years, but I have to say by completing the second course of this specialization, they helped me deepen my understanding, overcome fear of implementing math and equations line by line, fix my intuitions about deep learning, and most importantly erase all the superstitions! Bravo and excellent job.

by Glenn B

•May 31, 2018

Course material was great, however the use of Tensorflow in the exercises requires more background than provided in the short tutorial.

I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.

by Svetlana L

•Oct 22, 2019

I liked that the course gradually introduces more and more complexity and concepts without making your drown. Even though existing frameworks (e.g. tensorflow) can be used so that most of the complexity is hidden it is still required to understand why one method should be used rather than the other. This course I believe addresses this (as well as first in this specialisation). I still wish there was more information on details but probably all that is needed are external links to extra material.

by Robert K

•Nov 18, 2017

Fantastic course! You can experience short, easy to understand lectures, followed by plenty of opportunities to implement covered material, and most importantly create optimized image classifiers - like cats, dogs. I liked how up until the end of the course you had to implement everything from the scratch, not just using read-made frameworks. Finally, you are introduced into frameworks, but this deep understanding stays with you. 5/5 recommendation. Bye, I gotta finish the rest of specialization.

by Ferenc F P

•Mar 08, 2018

Good course explaining the concept of hyperparameters vs. parameters, how you can tune the hyperparameters, as well as different regularization techniques. It also provides good explanation for different optimization algorithms (enhancements to stochastic gradient descent). It is a highly recommended course for those who want to understand what is happening under the hood when using a neural network framework, like tensorflow. In the last week a brief introduction to tensorflow is also provided.

by Akanksha D

•Dec 31, 2017

The course is great as I expected. It would be helpful if more mathematical background in videos or notes can be attached in each weeks. Moreover, more code could be given to us to write by ourselves to get much better intuition. Rest each of the specializations are awesome as was the first learning Andrew Ng course on Machine Learning. Thank you for providing such courses. This is a great deal for all such students who cannot afford to attend Ivy leagues due to their own reasons.

Great Work!!

by Kévin S

•Jul 31, 2018

It explain neural network from the start. After doing all the 5 courses on deeplearning, it is hard to remember normalization formula, and every details. Sometime some hyperparameter look like a little bullshit: You don't know how to do : add one hyperparameter and go for an argmax. But if it is how its work, then it is okay to learn it; Be ready to laugth and do not compare to pure methods like genetics or Bayesian programming that often work good. But every one should follow this course.