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Build modern deep-learning models from the tensor up — and understand every line.
PyTorch stops feeling like magic when tensors, shapes, autograd, and training loops finally make sense. Build your understanding from the ground up with clear explanations, runnable code, and no GPU required.
Minimum price
$19.00
$29.00
About the Book
I wrote the PyTorch book I needed and couldn't find — one that explains why PyTorch exists, why it's different, and builds from the absolute ground up so nothing stays vague.
If you've ever stared at a tensor error, nodded along to a tutorial, and still felt like PyTorch was magic you couldn't reproduce on your own — this book is for you.
PyTorch From Ground Up takes you from complete beginner to confidently building real models — not by hand-waving, but by making you fluent in the thing every learner secretly struggles with: tensors and shapes. You'll master slicing, broadcasting, reshaping, squeezing, and permuting until shape errors stop scaring you. Then you'll build up through autograd, loss functions, and optimizers; assemble your own models with nn.Module; feed them with Datasets and DataLoaders; and write training loops you understand line by line — all the way to debugging, evaluation, and seeing inside your models.
Every concept is paired with short, runnable code. No GPU required — everything runs on your laptop's CPU or free Google Colab.
Written for self-learners with basic Python, and ideal for students and instructors who want a clear, complete, build-it-yourself path into deep learning.
Stop memorizing. Start understanding. Build from the ground up.
About the Author
Wesam M. Khallaf is an engineer with over 25 years
in software, automation, and control systems, and a
Master’s in Computer Science. He created the PyTorch
From Ground Up series to teach deep learning the
honest way — one concept, and one tensor, at a time.
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