Если вас интересует глубокое обучение (DL) и машинное обучение (ML), то в этой книге для вас есть интересные вещи. Моя цель в этой книге - дать вам широкие навыки, чтобы быть эффективным практиком машинного обучения и глубокого обучения. Эта книга предназначена для людей, которые хотят использовать машинное обучение и глубокое обучение в своей работе. Сюда входят программисты, художники, инженеры, ученые, руководители, музыканты, врачи и все, кто хочет работать с большими объемами информации, чтобы извлечь из нее смысл или создать новые данные.
If you’re interested in deep learning (DL) and machine learning (ML), then there’s good stuff for you in this book. My goal in this book is to give you the broad skills to be an effective practitioner of machine learning and deep learning.
This book is designed for people who want to use machine learning and deep learning in their own work. This includes programmers, artists, engineers, scientists, executives, musicians, doctors, and anyone else who wants to work with large amounts of information to extract meaning from it, or generate new data.
Many of the tools of machine learning, and deep learning in particular, are embodied in multiple free, open-source libraries that anyone can immediately download and use. Even though these tools are free and easy to install, they still require significant technical knowledge to use them properly. It’s easy to ask the computer to do something nonsensical, and it will happily do it, giving us back more nonsense as output.
You don't need any previous experience with machine learning or deep learning for this book. You don't need to be a mathematician, because there's nothing in the book harder than the occasional multiplication. You don't need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware.
Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure.
Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code.
The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice.
Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more.
Название: Deep Learning, Vol. 2: From Basics to Practice
Автор: Andrew Glassner
Издательство: Amazon Digital Services LLC
Формат: True PDF
Размер: 143.6 MB
Table of Contents: