PyTorch From Ground Up

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.