Voltar para Deep Neural Networks with PyTorch

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1,160 classificações

•

256 avaliações

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...

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 mai 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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por Lixy

•10 de ago de 2021

easy to understand

por Amir J

•12 de ago de 2020

Amazing course!

por arash h

•30 de nov de 2021

perfect course

por CHALLA K S N M S

•21 de set de 2020

awesome course

por Philipp A

•28 de jul de 2022

Great Course

por Manh D 1

•9 de mai de 2022

really good

por Aditya M P

•8 de dez de 2020

Good Course

por Panos K

•23 de mai de 2022

Excellent

por Oyenola P

•6 de jul de 2022

awesome

por Godwin M

•26 de set de 2021

AWESOME

por 徐淇

•3 de ago de 2021

good!

por Abdullaev S

•6 de mar de 2021

Coll!

por ASITHA I D

•15 de fev de 2021

Good.

por Marco C

•30 de mar de 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.

por Peter P

•8 de jul de 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.

por Benjamin K

•24 de abr de 2022

Despite the irritating computer voice and sloppy slides it is a good course. It is less a PyTorch course but an very nice introduction into ML and deep learning in general. Important concepts are introduced without overboarding the material with too much Math.

The labs could be more interesting and challenging. Towards the end the IBM Cloud was not working any more, before it was really convienent to do the labs in the browser. However, there are only a few requirements and anyone with a little Python experience can quickly setup a virtual environment. However, an instruction and a requirements.txt would be nice.

por Julien P

•11 de jun de 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.

por Farhad M

•24 de jun de 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.

por Felix H

•30 de jun de 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).

por Mitchell H

•2 de ago de 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.

por Carlos R

•28 de fev de 2022

Exceptional course. The lectures were little monotonous and robotic, I like this courses to be instructed by human speakers, but this did not affect the content of this course, the clarity on the topics and how well it was explained, it helped also to improve my knowledge on computer vision.

Great course.

por drygrass

•27 de dez de 2020

Very good fundamental course.

It will be good if real data is used in lab rather than using virtual data.

Also, the notebook's hyperlink of the final assignment isn't work. I can't import the notebook to Watson studio and finish the assignment, please fix it, thank you.

por Josephine J

•19 de jul de 2021

Explanation was confusing as time, and text-to-speech lecturer made it harder to engage. Lots of typos and unintuitive phrasing. However, taught useful skills, and all the resources were there to do own thinking/research and eventually understand everything.

por bob n

•13 de out de 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.

por Kaustubh S

•8 de jul de 2021

Good explanation with examples of code in python. The concept of convolution can be elaborated upon further as to it's genesis and how multiple processing techniques such as max pooling impact performance

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