(as of Dec 04,2021 16:31:49 UTC – Details)
From the Publisher
About This Book
In engineering disciplines, design patterns capture best practices and solutions to commonly occurring problems. They codify the knowledge and experience of experts into advice that all practitioners can follow. This book is a catalog of machine learning design patterns that we have observed in the course of working with hundreds of machine learning teams.
Who Is This Book For?
Introductory machine learning books usually focus on the what and how of machine learning (ML). They then explain the mathematical aspects of new methods from AI research labs and teach how to use AI frameworks to implement these methods.
This book, on the other hand, brings together hard-earned experience around the “why” that underlies the tips and tricks that experienced ML practitioners employ when applying machine learning to real-world problems.
We assume that you have prior knowledge of machine learning and data processing. This is not a fundamental textbook on machine learning. Instead, this book is for you if you are a data scientist, data engineer, or ML engineer who is looking for a second book on practical machine learning.
If you already know the basics, this book will introduce you to a catalog of ideas, some of which you may recognize, and give those ideas a name so that you can confidently reach for them. If you’re a computer science student headed for a job in industry, this book will round out your knowledge and prepare you for the professional world. It will help you learn how to build high-quality ML systems.
What’s Not in the Book
This is a book that is primarily for ML engineers in the enterprise, not ML scientists in academia or industry research labs.
We purposefully don’t discuss areas of active research—you will find very little here, for example, on machine learning model architecture (bidirectional encoders, or the attention mechanism, or short-circuit layers, for example) because we assume that you will be using a pre-built model architecture (Ex: ResNet-50 or GRUCell), not writing your own image classification or recurrent neural network.
Here are some concrete examples of areas that we intentionally stay away from because we believe that these topics are more appropriate for college courses and ML researchers:
ML algorithms — We do not cover the differences between random forests and neural networks, for example. This is covered in introductory machine learning textbooks.
Building blocks — We do not cover different types of gradient descent optimizers or activation functions. We recommend using Adam and ReLU—in our experience, the potential for improvements in performance by making different choices in these sorts of things tends to be minor.
ML model architectures — If you are doing image classification, we recommend that you use an off-the-shelf model like ResNet or whatever the latest hotness is at the time you are reading this. Leave the design of new image classification or text classification models to researchers who specialize in this problem.
Model layers — You won’t find convolutional neural networks or recurrent neural networks in this book. They are doubly disqualified—first, for being a building block and second, for being something you can use off-the-shelf.
Custom training loops — Just calling model.fit() in Keras will fit the needs of practitioners.
In this book, we have tried to include only common patterns of the kind that machine learning engineers in enterprises will employ in their day-to-day work.
As an analogy, consider data structures. While a college course on data structures will delve into the implementations of different data structures, and a researcher on data structures will have to learn how to formally represent their mathematical properties, the practitioner can be more pragmatic. An enterprise software developer simply needs to know how to work effectively with arrays, linked lists, maps, sets, and trees. It is for a pragmatic practitioner in machine learning that this book is written.
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Publisher:O’Reilly Media; 1st edition (November 10, 2020)
Item Weight:1.43 pounds
Dimensions:7 x 1 x 9.25 inches