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

4.4
étoiles
690 évaluations
151 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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101 - 125 sur 151 Avis pour Deep Neural Networks with PyTorch

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 Felix H

Jun 30, 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

Aug 02, 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 Jesus G

Jun 19, 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 TJ G

Jan 11, 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

May 10, 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

Jun 24, 2020

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

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 Patricio V

May 31, 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

Apr 05, 2020

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

par Вадим Н

Jul 19, 2020

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

par Krishna S B

Dec 27, 2019

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

par Youness E M

Dec 21, 2019

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

par Bilal G

Mar 29, 2020

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

par ASHWINPRASAD H

Jul 29, 2020

Great Course for beginners in pytorch

par harshita b

May 18, 2020

good explanation with examples

par Roberto G

Apr 12, 2020

very practical, lack of theory

par Lemikhov A

Feb 19, 2020

No programming assingments

par Mohd N K

May 14, 2020

very practical

par Richard B

May 17, 2020

Challenging

par Michael H

Jun 21, 2020

This course was not to the same standard as some others I've taken on Coursera. I think the concepts would have been very hard to follow if I hadn't already taken the Deep Learning specialization, so it isn't a great conceptual introduction to Deep Learning. That said, it also doesn't deeply explore the nuances of the PyTorch library, or give very much guidance on best practices or how it differs from other popular frameworks like Keras/TensorFlow. The quiz questions are fairly shallow (and often frustratingly ambiguous). Probably the best part of the class are the ungraded lab assignments.

par Olivier C

May 08, 2020

Useful if you are already comfortable with deep learning and you want to learn how to use the (great) pytorch package. If you want to learn about deep learning from scratch, the explanations are not very intuitive and skip over some very interesting features.

par Chaney O

Jun 02, 2020

The lectures and quizzes are too short to provide much value. The material could be better condensed. The labs were useful, although at times, it felt like the same material from a prior video. In general, it was a good overview.

par Mohammed A S

May 05, 2020

This course provides a good amount of knowledge of PyTorch. However, the explanation and presentation are really bad. The monotonous voice and the quick changing of slides forces learners to watch the videos again and again.

par Sabrina S

Mar 09, 2020

Ok walkthrought of pytorch, a lot of content but slight mismatch between rather basic DS topics and advanced programming skills. Materials need to be reviewed for spelling and grammar, some quiz questions are unclear.