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Opiniones y comentarios de aprendices correspondientes a Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization por parte de deeplearning.ai

4.9
34,179 calificaciones
3,650 revisiones

Acerca del Curso

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....

Principales revisiones

CV

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.

XG

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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1 - 25 de 3,605 revisiones para Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

por Brennon B

Apr 23, 2018

Walking away from this course, I do *not* feel adequately prepared to implement (end-to-end) everything that I've learned. I felt this way after the first course of this series, but even more so now. Yes, I understand the material, but the programming assignments really don't amount to more than "filling in the blanks"--that doesn't really test whether or not I've mastered the material. I understand that this is terribly hard to accomplish through a MOOC, and having taught university-level courses myself, I understand how much effort is involved in doing so in the "real world". In either case, if I'm paying for a course, I expect to have a solid grasp on the material after completing the material, and though you've clearly put effort into assembling the programming exercises, they don't really gauge this on any level. Perhaps it would be worth considering a higher cost of the course in order to justify the level of effort required to put together assessments that genuinely put the student through their paces in order to assure that a "100%" mark genuinely reflects both to you and the learner that they have truly internalized and mastered the material. It seems to me that this would pay off dividends not only for the learner, but also for the you as the entity offering such a certificate.

por Matthew G

Apr 18, 2019

Very good course. Andrew really steps it up in part two with lots of valuable information.

por Md. R K S

Apr 15, 2019

Excellent course. When I learned about implementing ANN using keras in python, I just followed some tutorials but didn't understand the tradeoff among many parameters like the number of layers, nodes per layers, epochs, batch size, etc. This course is helping me a lot to understand them. Great work Mr. Andrew Ng. :)

por oli c

Dec 09, 2018

Lectures are good. Quizzes and programming exercises too easy.

por Tang Y

Apr 15, 2019

very practical.

por Lien C

Mar 31, 2019

The course provides very good insights of the practical aspect of implementing neural networks in general. Prof. Ng, as always, delivered very clear explanation for even the difficult concepts, and I have thoroughly enjoyed every single lecture video.

Although I do appreciate very much the efforts put in by the instructors for the programming assignments, I can't help but thinking I could have learnt much more if the instruction were *LESS* detailed and comprehensive. I found myself just "filling in the blank" and following step-by-step instruction without the need to think too much. I'm also slightly disappointed with the practical assignment of Tensorflow where everything is pretty much written out for you, leaving you with less capacity to think and learn from mistakes.

All in all, I think the course could have made the programming exercise much more challenging than they are now, and allow students to learn from their mistakes.

por Harsh V

Jan 22, 2019

Add more programming assignments to clear fundamentals.

por Yuhang W

Nov 25, 2018

programming assignments too easy

por Ethan G

Oct 17, 2017

I did not think this was a great course, especially since it's paid. The programming assignment notebooks are very buggy and the course mentors are of varying quality. It feels more than a bit unfinished. It also covers two completely different topics - tools for improving deep learning nets and tensorflow - and doesn't make much of an effort to integrate them at all. The course could have used at least one more week of content and assignments to better explain the point of tf.

por Alan S

Sep 30, 2017

As far as the video lectures is concerned, the videos are excellent; it is the same quality as the other courses from the same instructor. This course contains a lot of relevant and useful material, and is worth studying, and complements the first course (and the free ML course very well).

The labs, however, are not particularly useful. While it's good that the focus of the labs is applying the actual formulas and algorithms taught, and not really on the mechanical aspects of putting the ideas in actual code, the labs have structured basically all of the "glue" and just leave you to basically translate formulas to the language-specific construct. This makes the lab material so mechanical as to almost take away the benefit.

The TensorFlow section was disappointing. It's really difficult to learn much in a 15 minute video lecture, and a lab that basically does everything (and oddly, for some things leaves you looking up the documentation yourself). I didn't get anything out of this lab, other than to get a taste for what it looks like. What makes it even worse is TensorFlow framework uses some different jargon that is not really explained, but the relevant code is almost given to you so it doesn't matter to get the "correct" answer. I finished the lab not feeling like I knew very much about it at all. It would have been far better to either spend more time on this, or basically omit it.

As with the first course, it is somewhat disappointing lecture notes are not provided. This would be handy as a reference to refer back to.

Still, despite these flaws, there's still a lot of good stuff to be learned. This course could have been much better, though.

por Raja s v

Jun 19, 2019

Great insight, practical approach. Really helpful course Thanks.

por Maryam H

Jun 19, 2019

prof. Ng's teaching was so great. some tricky details taught that I never considered them before. when I read the textbook, it was easy to understand and repetitive. I've learned simple and clean implementation. in overall it was important, simple, understandable, time efficient course.

por Tomas O H

Jun 19, 2019

All the practical knowledge in this course is amazing...

por Chen H

Jun 19, 2019

Great course, easy to understand with enough programming instructions

por

Jun 19, 2019

great courses

por Gaurab B

Jun 19, 2019

Brilliant material altogether.. almost a compulsory course for researchers diving on the ocean of deep learning.. While I was reading papers on deep learning I came across all these terms but couldn't understand it.. Now the picture is pretty clear... Thanks Prof. Andrew Ng for this wonderful effort. I have already recommended this course to everyone.

por Subasish K

Jun 18, 2019

Excellent course and a must one.

por Vasileios I

Jun 18, 2019

A good summary of Hyperparameter tuning, Regularization and Optimization. One suggestion would be to extend this course for 1-2 weeks and emphasize more on the intuitions and mathematics behind the optimizers. But again, the videos are awesome.

por Gerardo M L

Jun 18, 2019

The course is amazing, the instructor explains everything with a good level of comprehension. All the covered topics are easy to understand, and the tips given are valuable. The examples given are new including also the information seen in the previous course, so you have a review of parts of the content you have seen. Although he keeps using the cat example, he introduces new other applications that are useful.

I wish that the last assignment were a little bit harder, or that we could use our previous knowledge and complement it with this new, but I suppose that it is this way because of pedagogy and it focus on the topic.

por Aditya V S

Jun 18, 2019

This course has got more content than I asked for.

por Victor R

Jun 18, 2019

Amazing course so far! Loving every minute of it. Informative, challenging yet easy to understand at the same time. Highly recommend doing it!

por William G

Jun 18, 2019

Awesome course !

por Hanchen H

Jun 18, 2019

理论与实践相结合的的典范课程!

por Neil S

Jun 17, 2019

Wonderful course that teaches one the intricacies of training better models. It's also great when learning to implement a neural network through Tensor Flow for the last assignment and realizing that you have a good understanding of whats going on "under the hood".

por sudheer n D

Jun 17, 2019

As usual, Andrew Ng has explained the concepts in the simplest way possible. A little work outside the course can help a lot.