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Kursteilnehmer-Bewertung und -Feedback für Deep Neural Networks with PyTorch von IBM

4.4
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577 Bewertungen
125 Bewertungen

Über den Kurs

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...

Top-Bewertungen

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 von 126 Bewertungen für Deep Neural Networks with PyTorch

von Zhenzhou Z

Jul 01, 2020

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

von Ayush k

Jul 06, 2020

incredible course covering from basics to a satisfaction level

von Farhad A

Jun 16, 2020

It was well structured . Thank you

von Oscar A C B

Jun 10, 2020

Excellent! Just what I needed.

von Marco C

Mar 30, 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.

von Peter P

Jul 08, 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.

von Julien P

Jun 11, 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.

von Farhad M

Jun 24, 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.

von 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).

von 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.

von Theodore 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.

von 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.

von 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.

von 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.

von 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.

von Yanjie T

Apr 05, 2020

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

von Krishna S B

Dec 27, 2019

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

von Youness E M

Dec 21, 2019

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

von Bilal G

Mar 29, 2020

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

von harshita b

May 18, 2020

good explanation with examples

von Roberto G

Apr 12, 2020

very practical, lack of theory

von Lemikhov A

Feb 19, 2020

No programming assingments

von Mohd N K

May 14, 2020

very practical

von Richard B

May 17, 2020

Challenging

von 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.