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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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26 - 50 sur 161 Avis pour Deep Neural Networks with PyTorch

par Wanderson G d S

Mar 08, 2020

The course is very complete and the instructor demonstrates a lot of knowledge on the subject.

par Jessiedee M G

Apr 13, 2020

The contents are not that hard and best suited as introduction level in deep learning.

par Adolfo C Y

Apr 16, 2020

State of the art Course

I was expecting a more challenging project, 5 stars content

par RuoxinLi

Dec 09, 2019

Very Clear explanation and rich labs. The quiz can be more challenging

par Mevan E

Apr 28, 2020

Well prepared course. I got a full overview of working with PyTorch.

par Alexis b

Mar 28, 2020

Well prepared and interesting content taught with a clear voice !

par Mohammed A

May 01, 2020

I really enjoyed the course for its diversity and practicality

par Evgeniya O

May 04, 2020

A very nice course with clear explanations and good examples.

par 석박통합김한준

Mar 06, 2020

Excellent lecture! I appreciate your great work! Thank you!

par Stefan W

Jan 26, 2020

Great course with in depth material & hands-on learning.

par Mateus N

Apr 25, 2020

Good introductory course! Lots of exercises and samples

par Mohamed O A T

Mar 15, 2020

Highly recommended course for students

par Vittorino M

Dec 09, 2019

Aprendí muchísimo. Gracias.

par Pavan D

Nov 19, 2019

very intuitive and in depth

par Farrukh N A

Dec 09, 2019

Best course on AI

par ThanhTung

Dec 25, 2019

very helpful

par RICARDO H R

Jul 24, 2020

It is a nice course to get you into Pytorch and with some insightful views of how some ML algorithms work but adding to the most upvoted review, the synth voice dialogue sometimes doesn't make sense, the inflections on the speech are weird at times, it spells things that come from a text based explanation rather than someone speaking (things like spelling "I E for -for example- and C N N for convolutional neural network among many, many others)... sometimes the voice is talking about one thing and something else is highlighted on the video, time mismatch...

Many grammar mistakes, stuff left in the examples and quizes that doesn't make sense... definitely needs a redaction and content check.

par Marcin L

May 01, 2020

Practice sessions are organized in a tool that doesn't have enough computing power for training neural networks. The networks often take hours to train and you have to constantly monitor them because if you don't, the tool will automatically sign you out and you will lose your results.

I also don't like the mechanistic reading style (sounds like a bot reading), lack of human interaction doesn't seem to work for lectures.

par Mitchell L

Jul 15, 2020

This course had many flaws including that at the most basic it was riddled with errors, typos, and formatting issues.

Some more specific feedback is that this course seemed overly preoccupied with explaining math concepts or neural net architecture at a high level and glossing over much of the actual pyTorch specific programming.

The organization of the lectures make no sense, with separate lectures and labs for single class and multiclass versions of various models even though the functions all were built to handle multiple dimensions and so there was really no difference. Additionally because the lectures, lab, and quiz used all the same examples this means we would see the exact material presented over and over with no clear pedagogical reason.

Additionally the course seemed overly preoccupied with OOP to the point of replicating the functionality of several built in pyTorch classes obfuscating the actual material with no clear reason given for why we were creating our own version of extant classes.

Lastly, the quizes almost never asked any questions about pyTorch. Most of them were just the most basic questions about comprehending reading code. Things like "if input = 3 how many inputs are there?" or "which option is used for He initialization" and the options are like "He initialization or Xavier"

par Ankush K

Apr 30, 2020

Very good course only , complete the practical assignments they are important.

As for exam the question answers should be based on practical outputs, say make a model for

this dataset or so. Paste the result for the score.

par Giorgio G

Jun 04, 2020

Great course material and explanations, so far the best of the IBM specialization.

Great job Joseph!!

par Prasad C

May 23, 2020

Not to easy , not too shallow, a perfectly comprehensive course with catholic aspects covered.

par Patrick O

May 31, 2020

Excellent course! Highly recommend to anyone wanting to learn PyTorch.

par Mukul K

Mar 20, 2020

Great course for beginners in pytorch

par Lee Y Y

Feb 09, 2020

Easy-to-follow course for pytorch