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

4.9
35,037 Bewertungen
3,741 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 3,735 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 jose b

Jul 17, 2019

I love these courses. I recommend them to anyone! :)

von Juha J

Jul 17, 2019

First it was easy but then I really had to start using my brain

von Yelan T

Jul 17, 2019

This course is amazing! I will recommend my friends!

von Bill F

Jul 16, 2019

Lots of techniques and methods for optimizing a NN.

von Dipanjan C

Jul 16, 2019

mice

von Lalith M

Jul 16, 2019

Best course for learning how to tune the hyperparameters in the neural networks

von Cary C

Jul 16, 2019

Really impressed with this course....Andrew is one of the best teachers I've ever had and his Deep Learning course is one of the best courses I've taken from Coursera!

von Christian V

Jul 16, 2019

Perhaps more examples of tensor overflow with direct implementation of neural networks provided by the framework

von Aamir I

Jul 15, 2019

I am delighted to have completed this course, now I know how Hyperparametrs actually work and how to tune them.

von Jingyu Z

Jul 15, 2019

This Course is really good for the beginner of NN and deep learning. It tells me what to consider and how to consider for model build-up. I also like the quiz which helps me to check my concepts understanding, the coding practice is easy to understand and I can logically learn how to practice my understanding of this session. I also love the interview session with DL Heroes. This course is really inspiring.

von Timothy G

Jul 15, 2019

This course was very helpful learn so additional information on hyperparameters which help me out at work. Where I was tuning my own and was able to get 88%. After taking this course and implementing what I learned into auto tune my hyperparameters my accuracy went up to 91.33%.

von Gourav M

Jul 15, 2019

Great practice to get hands on programming skills in machine learning!

von Bohdan B

Jul 15, 2019

Andrew Ng my favorite lecturer

von Siddharth K

Jul 15, 2019

Need Information about other parameters like #of iterations, how to choose number of hidden layers?, number of neurons in hidden layers, inclusion of few other strategies to choose neural network model will be helpful. If they are covered in next courses, then please ignore.

Thanks

von daniele r

Jul 15, 2019

One of the best and most technical course in this Specialization: I enjoyed learning a lot on optimization algorithms. Really good practical hints on tuning and on bias variance analysis, that are very difficult to find in textbooks