Preface :)

I’m really excited to read and practice the contents of this book. Thanks for the authors.

“The next four chapters focus on modern deep learning techniques” should be “The next 5 chapters focus on modern deep learning techniques”

hello everyone, I feel very happy to improve my knowledge in deep learning. :slight_smile:

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Hello all!
Excited to start learning the book and hopes to learn the intricacies well.

Hello everybody!

I am really excited to start this new journey of learning for the first time MXNet and also learn more about DeepL. Hope I will be able to help other people too!

Hi everyone,

Very excited about this resource! Diving into the book immediately

Hi there, congratulations and thank you so much for the book.
I just started researching in the field, and this book is gonna be incredibly useful!

Mostly out of curiosity, I see there are basically two version of the book, based either on the NDArray or the numpy interface, and that the numpy version is recommended.
Why is that? Are numpy arrays more efficient?
I was wondering about it because I visited the GluonCV website, and most the tutorials there mainly use NDArrays. Which structure is generally more compatible with the libraries and better to use?

Thanks again,cheers!

Thanks!

We are on the progress of migrating to DeepNumpy. That’s why partial of our operators are still NDArrays. The new version is compatible with the numpy package, with a faster accelerator . Check here for more details. https://d2l.ai/chapter_preliminaries/ndarray.html

Thanks for this nice book!
By the way, I found a type in the Preface.
“For any block block such as a function, a class, or multiple imports to be saved in the package”
Is this the right place to report typos?

hello!
thanks for the book!

Great catch! Just changed! https://github.com/d2l-ai/d2l-en/pull/699

Yep this is the right place. You are also welcome to directly post a pull request at our github. :slight_smile:

Hi all,
This seems to be a great start point for learning.
Cheers!

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Hello from Michigan, US. I’m a little late to the party, but I finally made it! Can we call this fashionable late? Anyway, figured that this COVID mess might be a good time to go from start to finish with a complete ML experience.

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Hi,
I find this book very appealing and clever by combining theory, math, code, and web-based resources.
I’m very excited about starting my guided machine learning journey with this text. I’m looking for it with promising interest and high expectations.

Thank you the authors for the brilliant works!

Delighted to get going!

Looking forward to diving into this course !
:nerd_face:

Just checking in I am starting to read this book. Thanks for taking the time to write it, I had the same issue finding a good resource when I started looking around the internet for a ML resource.

Hi everyone,

I am glad to be here! Dive into deep learning!

Hello all, this is a good website to learn DL