دانلود کتاب آموزش گام به گام یادگیری عمیق با استفاده از PyTorch پایتون

I wrote this book for beginners in general—not only PyTorch beginners. Every now and then, I will spend some time explaining some fundamental concepts that I believe are essential to have a proper understanding of what’s going on in the code. The best example is gradient descent, which most people are familiar with at some level. Maybe you know its general idea, perhaps you’ve seen it in Andrew Ng’s Machine Learning course, or maybe you’ve even computed some partial derivatives yourself! In real life, the mechanics of gradient descent will be handled automatically by PyTorch (uh, spoiler alert!). But, I will walk you through it anyway (unless you choose to skip Chapter 0 altogether, of course), because lots of elements in the code, as well as choices of hyper-parameters (learning rate, mini-batch size, etc.), can be much more easily understood if you know where they come from.