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Avis et commentaires pour d'étudiants pour Deep Neural Networks with PyTorch par IBM

4.4
étoiles
728 évaluations
160 avis

À propos du cours

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered. Learning Outcomes: After completing this course, learners will be able to: • explain and apply their knowledge of Deep Neural Networks and related machine learning methods • know how to use Python libraries such as PyTorch for Deep Learning applications • build Deep Neural Networks using PyTorch...

Meilleurs avis

SY

Apr 30, 2020

An extremely good course for anyone starting to build deep learning models. I am very satisfied at the end of this course as i was able to code models easily using pytorch. Definitely recomended!!

RA

May 16, 2020

This is not a bad course at all. One feedback, however, is making the quizzes longer, and adding difficult questions especially concept-based one in the quiz will be more rewarding and valuable.

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76 - 100 sur 161 Avis pour Deep Neural Networks with PyTorch

par Zaheer U R

Jul 12, 2020

Amazing course with brilliant explanation

par Farhad A

Jun 16, 2020

It was well structured . Thank you

par Krishna H

Apr 28, 2020

Good!

par Ali A

Sep 14, 2020

The labs are simply taking so much time. I am sure the is a better way to teach students than to make them wait 1 hour. Some people would want to run them locally, but this is not a solution, just a bypass. I learning a lot in this course and would reccomend. The best thing is that it taught me that CNNs are not super tough and with proper techniques can be handled.

par Fabrizio D

Jul 30, 2020

Positive

-A lot of codes for practicing and learning

-The quizzes are short and focused

Negative

-The videos are too impersonal: it seems that the speaker is just reading the part, after a while I got tired of listening to him.

-Please review the texts: there are too many misspelled words

-Add more line of comments in the codes provided in lab

par Miele W

Feb 16, 2020

Well, as there are no sort of exams or real questions to answer in order to pass, it strictly depends on how much attention you put in following this course. IMHO if well studied, it gives you a solid foundation, in order to let you explore the pytorch module.

par Philippe G

Mar 10, 2020

Very interesting course. Gives a good introduction to pytorch. My only concern is the quality of the quizzes: It is often limited to 2 very simple questions. This does not allow you to validate that you had a good understanding of the said topic.

par Luca R

Mar 29, 2020

At the beginning, PyTorch framework seems very hard to understand. At the half of course you begin to have a clear vision of the problems. A negative point is the notebook for every topic. I would suggest one for week with everything inside.

par Eric

Jan 20, 2020

Good, thorough course. Does not hold the student to any kind of standard or accountability and quizzes are ridiculously easy to pass.

par Mateo P

Jul 10, 2020

The amount of material was surprisingly extensive and the labs were very useful. The tests were not very good. The videos were OK.

par Andrey G

Jun 17, 2020

The quizzes are way too easy. The videos are OK (read by computer voice except one). The labs, on the other hand, a really nice.

par Vitalii S

Apr 15, 2020

Pros:

Good intro to PyTorch, great work.

Cons:

1) typos along the course.

2) lab is working too slow - better run locally.

par Paranjape A J

Feb 13, 2020

More graded coding assignments would have been better, but content is good!

par Aditya L

Aug 12, 2020

I had very high expectations for this course since it was offered by IBM and being taught by someone with excellent credentials. I completed the course material for the first 2 weeks and I found the lectures to me unmotivating, inadequately explained, and very clearly the lecturer read from a script. Important concepts were not explained neither the conceptual deep learning one nor the PyTorch programming ones. They were very briefly explained often with one short sentence. I thought the ungraded labs were very well designed but the lecture quality was so poor, it seemed I was just googling and learning 90% of PyTorch myself. I had expected quality from this course however, I did not get it so I decided not to pay the $50 subscription and canceled the course. I was disappointed since I did spend good 15-20 hours on this course.

par Tarun C

Apr 03, 2020

This course is a disorganized and unfocused. For example, much of the section on Bernoulli distribution is misleading or completely incorrect. It's also presented without context. Much of this is redundant give the other courses in this certificate program do a much better job of teaching ML concepts. The novelty of this course is about implementation using pytorch and most of the important details about how to use PyTorch and why certain parameters are used are glossed over.

Is this a course about ML and Neural Networks? Is this a course on PyTorch? It does both poorly.

Please see

https://www.coursera.org/mastertrack/instructional-design-illinois

for how to improve.

par sada n

Jan 10, 2020

it is too deep

par A A A

Jul 07, 2020

This course is really good in explaining the concepts and pytorch. Everything was explained in a detailed way, well structured. However, I found the course too segmented. Some lectures, some quizzes, and some labs can be combined. Example for week 1, I think 1.1 (introduction to tensors), 1.2 (1d tensors) and 1.3 (2d tensors) can be combined to single lecture or all 3 lectures be one after another making it appear like it’s together. The 2 labs can be combined into a single notebook. The 2 quizzes can be combined into 1 quiz of maybe 5 or more questions. Similarly, 1.4 (Simple Datasets) and 1.5 (Datasets) can be combined, and so on. I also think that the honours content about batch normalization should be included as part of normal contents. Maybe more advanced concepts can be put up as honours contents.

par Erdem Ş

Jun 17, 2020

even with no mandatory peer graded assignment, for me it was the hardest course to learn in "IBM AI Engineering". So many topics and so many codes to check for each week. i liked it. i believe i will revisit the materials in the future.

par Georgios C

Aug 04, 2020

Great introduction to deep learning with pytorch. It would help if the notebooks in the labs take shorter to run so that the students can experiment with the code and the models.

par Adil D

Oct 29, 2020

Really good course!! Theres few typos in the video lectures but a good way to see if you really understand things ;)

par Vaseekaran V

Oct 23, 2020

Really great intro to PyTorch. Well explained the basics of Deep Learning along with PyTorch.

par Integral S

Aug 09, 2020

this is no doubt THE BEST and the most well thought pytorch and deep learning course so far .

par Hasan G

Aug 21, 2020

I have learned good skills for deep neural networks

par Luis C

Aug 17, 2020

best introduction course on the subject.

par Oscar A C B

Jun 10, 2020

Excellent! Just what I needed.