دانلود کتاب یادگیری عمیق کاربردی، مقدمه‌ای مبتنی بر پایتون

I wrote this book for readers who have no background in machine learning, but who are curious and willing to experiment. I’ve kept the math to a minimum. My goal is to help you understand core concepts and build intuition you can use going forward. At the same time, I didn’t want to write a book that simply instructed you on how to use existing toolkits but was devoid of any real substance as to why. While you can build models while caring only about the how, without the why, you’ll be parroting rather than understanding, let alone moving the field forward with your own contributions. As far as assumptions on my part, I assume you have some familiarity with computer programming, in any language. The language of choice for machine learning, whether you are a student or a major corporation, is Python, so that’s the language we’ll use in this book. I’ll also assume you’re familiar with high school math, excluding calculus. A little calculus will creep in anyway, but you should be able to follow the ideas, even if the technique is unfamiliar. I’ll also assume you know a bit of statistics and basic probability. If these concepts are new to you, or you are a bit rusty, Chapter 0 includes a brief summary. You can also dive deeper into these topics with my book Math for Deep Learning (No Starch Press, 2021). Finally, deep learning in Python makes heavy use of the NumPy library, which extends Python by adding high-speed array-processing abilities ideally suited to scientific programming. If NumPy is new to you, you’ll find a brief tutorial on the book’s GitHub site, or you can dive deeper with Python Tools for Scientists by Lee Vaughan (No Starch Press, 2022).