What should you expect from this book?

You've begun to read a book that focuses on teaching some of the advanced modeling techniques that've emerged in recent years. This book is aimed at anyone who wants to learn about those algorithms, whether you're an experienced data scientist or developer looking to parlay existing skills into a new environment.

I aimed first and foremost at making sure that you understand the algorithms in question. Some of them are fairly tricky and tie into other concepts in statistics and machine learning.

For neophyte readers, I definitely recommend gathering an initial understanding of key concepts, including the following:

• Neural network architectures including the MLP architecture
• Learning method components including gradient descent and backpropagation
• Network performance measures, for example, root mean squared error
• K-means clustering

At times, this book won't be able to give a subject the attention that it deserves. We cover a lot of ground in this book and the pace is fairly brisk as a result! At the end of each chapter, I refer you to further reading, in a book or online article, so that you can build a broader base of relevant knowledge. I'd suggest that it's worth doing additional reading around any unfamiliar concept that comes up as you work through this book, as machine learning knowledge tends to tie together synergistically; the more you have, the more readily you'll understand new concepts as you expand your toolkit.

This concept of expanding a toolkit of skills is fundamental to what I've tried to achieve with this book. Each chapter introduces one or multiple algorithms and looks to achieve several goals:

• Explaining at a high level what the algorithm does, what problems it'll solve well, and how you should expect to apply it
• Walking through key components of the algorithm, including topology, learning method, and performance measurement
• Identifying how to improve performance by reviewing model output

Beyond the transfer of knowledge and practical skills, this book looks to achieve a more important goal; specifically, to discuss and convey some of the qualities that are common to skilled machine learning practitioners. These include creativity, demonstrated both in the definition of sophisticated architectures and problem-specific cleaning techniques. Rigor is another key quality, emphasized throughout this book by a focus on measuring performance against meaningful targets and critically assessing early efforts.

Finally, this book makes no effort to obscure the realities of working on solving data challenges: the mixed results of early trials, large iteration counts, and frequent impasses. Yet at the same time, using a mixture of toy examples, dissection of expert approaches and, toward the end of the book, more real-world challenges, we show how a creative, tenacious, and rigorous approach can break down these barriers and deliver meaningful results.

As we proceed, I wish you the best of luck and encourage you to enjoy yourself as you go, tackling the content prepared for you and applying what you've learned to new domains or data.

Let's get started!