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Volver a Deep Neural Networks with PyTorch

Opiniones y comentarios de aprendices correspondientes a Deep Neural Networks with PyTorch por parte de Habilidades en redes de IBM

4.4
estrellas
1,158 calificaciones
256 reseña

Acerca del Curso

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...

Principales reseñas

SY

29 de abr. de 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 de may. de 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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51 - 75 de 258 revisiones para Deep Neural Networks with PyTorch

por Roger S P M

31 de mar. de 2020

The course material contains some really fantastic information, graphics, and programming assignments. However, the presentation of this material is absolutely terrible! It seems they intentionally tried to make the presentations as boring as possible. The lectures are monotone, the 15 second opening scene is annoying, and the content focuses 70% on the concepts of Deep Learning (which is fine) and 30% on PyTorch. So when you finish you do not feel very skilled with PyTorch.

Finally, ALL of the student complain that the programming environment is very often offline. You cannot do many of the assignments because the "Cognitive Classroom" is usually not working. However, the last lecture f each week contains the Jupyter notebooks for the assignments. You can download and then run them in some other environment like Google Colaboratory or IBM Watson Cloud. Also, most of the programs contain a programming omission that the students have to fix every time. The instructors have not fixed the problem which has been reported to them. So pay attention for the "Pillow Error" in Week 3 because you will be fixing it yourself in most assignments for the next 4 weeks.

por Mitchell L

15 de jul. de 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"

por Mohamed O A T

15 de mar. de 2020

Highly recommended course for students

por Lee Y Y

9 de feb. de 2020

Easy-to-follow course for pytorch

por Suan S A C

8 de abr. de 2020

I really enjoy this course!!!

por Shreya D

2 de may. de 2020

very well structured course.

por Vittorino M

9 de dic. de 2019

Aprendí muchísimo. Gracias.

por Irfan S

31 de may. de 2020

Labs were detailed one.

por David S

29 de mar. de 2020

Fantastic explanation

por Marvin L

6 de feb. de 2020

It was Good !!

por Divyansh C

20 de nov. de 2020

I appreciate this course. Its really amazing course and if you are a beginner in Deep Learning and want to use and learn Pytorch then this course is really good to start.

One thing about this course is that some important topics like RNN, R-CNN , text and sentiment analysis, time series are not included in this course which I think should be included.

por RICARDO H R

24 de jul. de 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.

por Juho H

6 de may. de 2020

This course is difficult to rate as a learning experience. There are some very good parts yet there is also some very poor material. I would say that if you are already very familiar with machine learning and Python BEFORE taking this course, you can still draw some useful learnings on how PyTorch can be applied to various problems, and how to create convolutional neural networks with it; but if you are uncertain about some of the key concepts, this course may only end up making things worse for you.

To give an idea of the problems, there are issues like:

- When explaining the train/validation/test data logic and how validation data can be used to prevent overfitting, the videos keep calling training data test data.

- Pytorch is used for some really fancy stuff like defining functions and datasets, but then those functions are not parametrized in any sensible way – meaning if you want to compare loss functions from two different initialisations of the model weights, you are expected to define a new function so you can just change the variable “LOSS” to “LOSS2”, rather than just passing the loss function as a parameter or just initializing or returning it. Given the Pytorch logic is not your regular Python stuff, a best practice should be provided – it is definitely not writing a new function every time.

So be warned: if you know what you are doing, and simply want to learn how to do it with Pytorch, this may still be a decent course for you, just ignore all the stuff where the instructors make mistakes (and they are plenty, also in incorrect quiz answers). But if you feel at all uncertain, I suggest you hone your machine learning skills elsewhere, because otherwise this course will leave you totally confounded on even the very basics of machine learning.

On the upside then, you learn Pytorch through repetition. In the beginning, the logic appears very intimidating, but then you gradually learn the logic and you can do some very impressive stuff quite easily in the end. Be prepared for the amount of repetition, however - first the stuff is shown on a video, then you run the exactly same stuff in a lab, and unfortunately the Skills Lab is not at all efficient for some of the stuff - I ended up downloading the notebooks and using them on my Watson Studio account for much faster performance.

por Daan S

18 de nov. de 2021

To be honest I am severely disappointed by the quality of the course. Nearly every single video contained typos and the example code often lacked consistency through weeks. For example, one week batch normalization was applied before activation, while the next week it was applied after activation. Without even elaborating on such changes, this threw me off as I am now unsure how to apply it. Furthermore, the labs barely presented any actual practice. In 9/10 cases I could just run all the code without implementing anything myself, this definitely decreased the learning experience. In addition, the quizzes don't provide any challenge at all. You can easily complete most quizzes without even watching the lectures as the answer is often already provided in the question itself. The last thing I would like to mention is that the staff in the discussion forums, although friendly, is clearly lacking fluency in English. They often don't seem to grasp the question and provide a copy-paste solution to most cases. Whether it's Deep Learning or PyTorch you want to learn, you're much better off following a course by a different provider on Coursera.

por Lennart F

28 de sep. de 2021

T​he video's are voiced by a robot. There is alot of information, but the quiz questions are so simple that you get the feeling they are aimed at 6-year olds (e.g. most times you just have to repeat what the robot voice has JUST said). The peer review system for the honors assignment is retarded: I failed this assignment while answering everything correctly, just because some dude accidentaly misgraded a question. Coursera now expects me to pay another 40 euros to resubmit the assignment just because someone else messed this up, lol. Take your courses elsewhere.

por Marcin L

1 de may. de 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.

por Konstantin S

24 de feb. de 2020

Poorly prepared materials, awful quiz modules, lots of mistakes

por Christian T

9 de jun. de 2021

Lots of errors in the questions and answers, annoying content structure, bad videos (speed, cadence, auto-generated voice that consistently mis-pronounces things). Labs that are identical to the videos. No context setting or understanding beyond trivial mechanics.

E​ven worse, the quizzes contain typing/syntax errors that you have to ignore and then suddenly some of the quizzes contain errors that you must not ignore.

T​his is a ridiculuously bad course and I have no idea how it got to getting this many good ratings.

A​BSOLUTE WASTE OF TIME. CHOOSE A DIFFERENT COURSE!

por Amar S

22 de ago. de 2020

I am very disappointed with the quality of the course materials. The videos are recorded with what sounds like a text to speech system or a voice over done by a voice actor who does not really understand the subject matter and lacks personality.

It's hard to understand as it all runs at the same pace and there isn't sufficient time given to specific concepts that may take a shorter or a longer time to sink in depending on their complexity. It's just a constant speed monologue without any real feeling or passion in the subject matter.

por Karishma D

21 de jul. de 2020

The right level of detail so that you can dive in.

I wish there had been a week to cover RNNs as well though, in particular the best way to handle variable length sequences for RNNs :)

por Surya P S e

27 de jul. de 2020

Wonderful course!!! Best among all the courses under AI Engineer Certificate by IBM. Deep learning always haunted me with the maths involved but now I get a very good start with this.

por Diego D

12 de jul. de 2020

Excellent Course. I love the way the course was presented. There were a lot of practical and visual examples explaining each module. It is highly recommended!

por Okta F S

18 de jun. de 2020

By this course I can understand the basic concept for building neural network or deep lerning model using PyTorch. Very Good course to beginner.

por Zhenzhou Z

1 de jul. de 2020

It would be better to add a section explaining the experiment code of the famous paper.

por Siladittya M

23 de jul. de 2020

Quiz questions are very easy. Graded Programming Assignments would have been better.