Practical Machine Learning
About This Book
Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
Comprehensive practical solutions taking you into the future of machine learning
Go a step further and integrate your machine learning projects with
Using Docker: Developing and Deploying Software with Containers
Docker containers offer simpler, faster, and more robust methods for developing, distributing, and running software than previously available. With this hands-on guide, you’ll learn why containers are so important, what you’ll gain by adopting Docker, and how to make it part of your development...
Microservices: Flexible Software Architecture
The Most Complete, Practical, and Actionable Guide to Microservices
Going beyond mere theory and marketing hype, Eberhard Wolff presents all the knowledge you need to capture the full benefits of this emerging paradigm. He illuminates...
Docker in Practice
An open source container system, Docker makes deploying applications painless and flexible. Docker is powerful and simple to use, and it makes life easier for developers and administrators alike providing shorter build times, fewer production bugs, and effortless application roll-out.
Data Science and Complex Networks: Real Case Studies with Python
This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web...
TypeScript Design Patterns
This step-by-step guide will would demonstrate all the important design patterns in practice
This book is the only documentation on the market focusing on design patterns in TypeScript
This book is packed with rich examples that will improve...
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