Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...
Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. This book takes a practical approach to data structures and algorithms, with techniques and real-world scenarios...
Graph Theory, Combinatorics and Algorithms:Interdisciplinary Applications focuses on discrete mathematics and combinatorial algorithms interacting with real world problems in computer science, operations research, applied mathematics and engineering. The book contains eleven chapters written by...
An internationally best-selling, conceptual introduction to the TCP/IP protocols and Internetworking, this book interweaves a clear discussion of fundamentals and scientific principles with details and examples drawn from the latest technologies. Leading author Douglas Comer covers layering and packet formats for all the...
Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve...
IoT Security Issues looks at the burgeoning growth of devices of all kinds controlled over the Internet of all varieties, where product comes first and security second. In this case, security trails badly. This book examines the issues surrounding these problems, vulnerabilities, what can be done to solve the problem, investigating the stack...
“I have found this book to be a very useful classroom text, as well as a great Linux resource. It teaches Linux using a ground-up approach that gives students the chance to progress with their skills and grow into the Linux world. I have often pointed to this book when asked to recommend a solid
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...
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Proven security tactics for today's mobile apps,devices, and networks
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural...
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more...
