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Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai

4.9
34,179 Bewertungen
3,650 Bewertungen

Über den Kurs

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....

Top-Bewertungen

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 of 3,640 Reviews for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

von 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.

von Matthew G

Apr 18, 2019

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

von oli c

Dec 09, 2018

Lectures are good. Quizzes and programming exercises too easy.

von 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. :)

von Tang Y

Apr 15, 2019

very practical.

von 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.

von Harsh V

Jan 22, 2019

Add more programming assignments to clear fundamentals.

von Yuhang W

Nov 25, 2018

programming assignments too easy

von 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.

von 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.

von Anuj D

Jun 26, 2019

Very important course for great practical tips on implementing neural networks.

von Mark P

Jun 26, 2019

very quick moving but the assignments were too easy - they give you too much of the code (both the surrounding code which is fine but also the precise code for running optimisers for example.

von saurabh h

Jun 26, 2019

Great course ! Lot of things covered.

Expecting more on the Tensorflow Framework.

von Keanu T

Jun 26, 2019

I wish it went a little more in-depth with softmax classifiers but I can find that online so it's good.

von Amir H

Jun 25, 2019

The explanation and examples are very informative throughout the course. The quizzes and the assignments are highly related to the topics covered in the videos which provide a solid understanding of the course.

von Yassine L

Jun 25, 2019

Loved this course, goes into details and helps you develop intuition into how NN work.

von Christian B

Jun 25, 2019

Good balance between theory and practice, focusing on the impact of hyperparameters across the whole solution.

von Omar H E K

Jun 24, 2019

The topics were advanced and practical, I am impressed

von Kiran R

Jun 24, 2019

This is an extremely important course, as it deals with lots best practices and nuts and bolts of Deep Learning that is the result of years of expertise and hard to find elsewhere

von Moaz M T

Jun 24, 2019

Exceptionally awesome course!

von Panadis N

Jun 24, 2019

He is the great instructor. He can explain difficult things to become easy.

von Virender p s

Jun 24, 2019

Thank You for this course!

von Marcos C C

Jun 24, 2019

Great! course!!!

von Dimitry I

Jun 23, 2019

Great follow-up course in the Deep Learning specialization. Teaches you important aspects of machine learning. Thank you to Prof Ng and the rest of the team!

von Taylor B

Jun 23, 2019

I took the Machine Learning Course from Stanford with Andrew Ng a few years ago and enjoyed it but I was also somewhat overwhelmed by the math. In contrast, this is my second course in the deep learning specialization and I feel like so far the courses have struck a good balance, introducing core concepts and derivations for things but also making sure I get guided practice along the way, and also not moving straight to frameworks but having students code more or less from scratch first. I'll probably need some practice on kaggle or other datasets as well as reference to a few other learning materials to feel like a strong practitioner, but this gives the tools to make that possible and I'm very satisfied with this result.