Название: Practical Deep Learning: A Python-Based Introduction, Second EditionАвтор: Ronald T. Kneusel
Издательство: No Starch Press
Год: 2025
Страниц: 669
Язык: английский
Формат: epub (true)
Размер: 25.5 MB
Dip into Deep Learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel.
After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you’re a developer looking to add AI to your toolkit or a student seeking practical Machine Learning skills, this book will teach you:
How neural networks work and how they’re trained
How to use classical machine learning models
How to develop a deep learning model from scratch
How to evaluate models with industry-standard metrics
How to create your own generative AI models
Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you’ll gain the skills and confidence you need to build real AI systems that solve real problems.
New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).
Who This Book Is For:
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.
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.
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