From the Preface

Who Should Read This Book?

As opposed to starting out with toy examples and building around those, we chose to start the book with a series of fundamentals to take you on a full journey through deep learning.

We feel that too many books leave out core topics that the enterprise practitioner often needs for a quick review. Based on our machine learning experiences in the field, we decided to lead-off with the materials that entry-level practitioners often need to brush up on to better support their deep learning projects.

You might want to skip Chapters 1 and 2 and get right to the deep learning fundamentals. However, we expect that you will appreciate having the material up front so that you can have a smooth glide path into the more difficult topics in deep learning that build on these principles. In the following sections, we suggest some reading strategies for different backgrounds.

The Enterprise Machine Learning Practitioner

We split this category into two subgroups:

- Practicing data scientist.

- Java engineer.

The Practicing Data Sceintist

This group typically builds models already and is fluent in the realm of data science. If this is you, you can probably skip Chapter 1 and you’ll want to lightly skim Chapter 2. We suggest moving on to Chapter 3 because you’ll probably be ready to jump into the fundamentals of deep networks.

The Java Engineer

The Enterprise ExecutiveJava engineers are typically tasked with integrating machine learning code with production systems. If this is you, starting with Chapter 1 will be interesting for you because it will give you a better understanding of the vernacular of data science. Appendix E should also be of keen interest to you because integration code for model scoring will typically touch ND4J’s API directly.

The Enterprise Executive

Some of our reviewers were executives of large Fortune 500 companies and appreciated the content from the perspective of getting a better grasp on what is happening in deep learning. One executive commented that it had 'been a minute' since college, and Chapter 1 was a nice review of concepts. If you’re an executive, we suggest that you begin with a quick skim of Chapter 1 to re-acclimate yourself to some terminology. You might want to skip the chapters that are heavy on APIs and examples, however.

The Academic

If you’re an academic, you likely will want to skip Chapters 1 and 2 because graduate school will have already covered these topics. The chapters on tuning neural networks in general and then architecture-specific tuning will be of keen interest to you because this information is based on research and transcends any specific deep learning implementation.

The coverage of ND4J will also be of interest to you if you prefer to do high-performance linear algebra on the Java Virtual Machine (JVM).