Название: Practical Deep Learning: A Python-Based Introduction
Автор: Ronald T. Kneusel
Издательство: No Starch Press
Год: 2021
Страниц: 464
Язык: английский
Формат: epub
Размер: 10,1 MB

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.

If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further.

All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance.

You’ll also learn:

• How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines
• How neural networks work and how they’re trained
• How to use convolutional neural networks
• How to develop a successful deep learning model from scratch
You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned.

The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.

Who Is This Book For?
I wrote this book for readers who have no background in machine learning, but who are curious and willing to experiment with things. 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 the why of things. It’s true that if all you care about is the how, you can build useful models. But without the why, you’ll only be parroting, not understanding, let alone eventually 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. I’ll also assume you’re familiar with high school math but not 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 you’ve forgotten those topics since high school, don’t worry—you’ll find relevant sections in Chapter 1 that give you enough of a background to follow the narrative.