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

4.4
étoiles
720 évaluations
157 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
29 avr. 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
15 mai 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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101 - 125 sur 157 Avis pour Deep Neural Networks with PyTorch

par Marco C

30 mars 2020

The course is good and has a nice mixture of theory and practice, which is essential for mastering complex concepts. However, I do have a few observations about the course quality:

- Several of the slides in the presentations and even the labs have a lot of grammar mistakes.

- The theory is often rushed in the lectures. The course would greatly benefit from a more careful analysis of the maths behind each concept.

-In its effort to make the concepts easier to grasp, the lectures keep using coloured boxes to replace mathematical terms. I found that to be more confusing, they use far too many colours and are too liberal with their use.

-Lastly, the labs completely broke down in the second half of the course. My understanding from the course staff is that an upgrade was made on the backend which did not go well and thus caused those issues. They should have several backup plans for those occurrences, starting with having the labs available for download so that the students can do them offline.

Overall I'm happy with the course and would cautiously recommend it, given the above shortcomings.

par Peter P

8 juil. 2020

The course was fantastic for someone like me. I already knew all the math, and the course gave deep exposure to the needed Python routines and classes. The labs really help cement the knowledge.

Only drawback is that it went a bit too slow for me (NN with one input, NN with two inputs, NN with one output, NN with two outputs, etc.), but others might disagree.

I'm giving it a four because there were so many typos and mistakes (i.e. the gradient is perpendicular to countour lines, not parallel), lots of mispellings and wrong data on the slides and the speaker sounded like a computer (he pronounced the variable idx as "one-dx" - huh? I understand that there's going to be mistakes, but this is an one online course made for many people, and you'd expect that kind of stuff to be corrected over time since it is being repeatedly delivered.

But - it was a great course and I highly recommend taking it.

par Julien P

11 juin 2020

Here is a list of pros and cons:

Pros: great notebooks and many examples

Cons: the videos are a bit "cheap" (typos and artificial voice) and often miss the intuitions ("To do that, we code like this"). A bit light on the maths. Quizzes are too easy to validate (people may validate with a superficial understanding of what is going on).

Summary: The value of this class resides in the notebooks and in the time your are willing to invest in them.

par Farhad M

24 juin 2020

I think it's a good course if you're coming in with the notion of deep learning pretty much clear and are more interested in learning the PyTorch syntax. I'm not sure how useful the course would be in terms of learning ML or DL from scratch. In particular the conceptual slides could be better.

The notebooks are well-prepared. Even though occasional bugs can be found, they aren't much to worry about.

par Felix H

30 juin 2020

The course gave a decent and well-structured introduction to PyTorch. However, I would have hoped for less typos (including in the code on the slides), more challenging and instructive quizzes and real exercises (there are instructive labs, but the practice section is usually only a very slight modification of the already given code).

par Mitchell H

2 août 2020

Awesome course for learning the basics/fundamentals of Pytorch. However the labs often would not run some of the more complex or CPU-intensive models, so I would suggest downloading the labs to your local machine. Also could have also used more assignments for hands-on experience, but I would recommend this course.

par bob n

13 oct. 2020

Concepts presented in nice bite size chunks. Labs help reinforce concepts. BUT, felt like course was just a bunch of pieces with little assembly. Kinda like finding a box of LEGOs (r) with nothing to really build from them.

par Edward J

18 oct. 2020

I learned loads in this course. I'm quite familiar with Keras so it was good to use a different package. The instruction was very clear but LONG. I would have liked the labs to have been more involved.

par Jesus G

19 juin 2020

A nice landing on Pytorch and basic Deep Learning concepts. I liked the collection of code and practical examples. If only, I missed having more difficult practical assignments along the course.

par Evgeniya K

20 sept. 2020

Good to dive into Deep Learning and get some PyTorch basics. However, there're sometimes mistakes in the assignments. Also, the explanations can sometimes be a bit confusing.

par TJ G

11 janv. 2020

Very intensive course. Could do more training labs. But this is definitely a very dense course. Extremely helpful to get started on ML/Deep Learning.

par Jian P

10 mai 2020

Good introduction of PyTorch. There are some minor code errors and inconsistencies in the material but generally not difficult to figure it out.

par Mehrdad P

24 juin 2020

The courses provides basic knowledge, but I wish that it was a bit more advanced and had more challenging assignments.

par Patricio V

31 mai 2020

Some of the courses are quite harsh, but finally come all togheter and there's a light at the end of the tunnel.

par Yanjie T

5 avr. 2020

the course is good, detailed, and practical, but the shortcoming is the lab quality, need to be imporved

par Вадим Н

19 juil. 2020

generally, the course is well but tasks too easy for "intermediate" level

par Krishna S B

27 déc. 2019

It would have been better if graded programming assignments were there.

par Youness E M

21 déc. 2019

There is a number of errors in the courses and in quiz

par Bilal G

29 mars 2020

less one star due to the many errors I noticed in the

par H. A P

28 juil. 2020

Great Course for beginners in pytorch

par harshita b

18 mai 2020

good explanation with examples

par Roberto G

12 avr. 2020

very practical, lack of theory

par Lemikhov A

19 févr. 2020

No programming assingments

par Mohd N K

14 mai 2020

very practical

par Richard B

16 mai 2020

Challenging