||Switching to the Mac: The Missing Manual, El Capitan Edition|
Those who have made the switch from a Windows PC to a Mac have made Switching to the Mac: The Missing Manual a runaway bestseller. The latest edition of this guide delivers what Apple doesn't - everything you need to know to successfully and painlessly move your files and adapt to Mac's way of doing things. Written with wit and objectivity by Missing Manual series creator and bestselling author David Pogue, this book will have you up and running on your new Mac in no time.
||Introduction to Machine Learning with Python|
Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Introduction to Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.
You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.
||Designing Machine Learning Systems with Python|
Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles.
There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
||Learning Proxmox VE|
Proxmox VE 4.1 provides an open source, enterprise virtualization platform on which to host virtual servers as either virtual machines or containers.
This book will support your practice of the requisite skills to successfully create, tailor, and deploy virtual machines and containers with Proxmox VE 4.1. Following a survey of PVE's features and characteristics,this book will contrast containers with virtual machines and establish cases for both. It walks through the installation of Proxmox VE, explores the creation of containers and virtual machines, and suggests best practices for virtual disk creation, network configuration, and Proxmox VE host and guest security.Throughout the book, you will navigate the Proxmox VE 4.1 web interface and explore options for command-line management.
||F# for Machine Learning Essentials|
The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs.
If you want to learn how to use F# to build machine learning systems, then this is the book you want.
Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.
||Blender 3D Incredible Machines|
Blender 3D is one of the top pieces of 3D animation software. Machine modeling is an essential aspect of war games, space games, racing games, and animated action films. As the Blender software grows more powerful and popular, there is a demand to take your modeling skills to the next level. This book will cover all the topics you need to create professional models and renders.
This book will help you develop a comprehensive skill set that covers the key aspects of mechanical modeling. Through this book, you will create many types of projects, including a pistol, spacecraft, robot, and a racer. We start by making a Sci-fi pistol, creating its basic shape and adding details to it. Moving on, you'll discover modeling techniques for larger objects such as a space craft and take a look at how different techniques are required for freestyle modeling.
After this, we'll create the basic shapes for the robot and combine the meshes to create unified objects. We'll assign materials and explore the various options for freestyle rendering. We'll discuss techniques to build low-poly models, create a low-poly racer, and explain how they differ from the high poly models we created previously.
||Learn Swift 2 on the Mac, 2nd Edition|
Swift is Apple's new, native, fast, and easy to learn programming language for iOS, watchOS, tvOS and OS X app development. It's their "Objective-C without the C". If you are an iOS developer or planning to become one, learning Swift is your #1 priority, and Learn Swift 2 on the Mac tells you everything you need to get up to speed, well, swiftly. The language is evolving very quickly, Apple has released version 2.1 of the langugae.
You'll start with the Swift Playground and an introduction to object-oriented programming so you can immediately see Swift in action. You then learn about all of the key language features like functions, closures, protocols, classes, methods, extensions, and how Swift works just as well as Objective-C when it comes to easy memory management with ARC.
Finally, you'll learn how to use Swift alongside Objective-C as well as with Core Data, and you'll learn how to put all of the pieces together with REST base application.
||Scala Data Analysis Cookbook|
This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits.
Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you'll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX.
||Swift 2 Design Patterns|
Swift is a multi-paradigm language. It has expressive features familiar to those used to work with modern functional languages, while also keeping the object-oriented features of Objective-C. It remains compatible with Apple's legacy codes and frameworks. A design pattern systematically names, motivates, and explains a general design that addresses a recurring design problem in object-oriented systems. It describes the problem, the solution, when to apply the solution, and its consequences. It also gives implementation hints and examples. Knowledge about design patterns is also one of the best ways to make you different compared to other low-level developers.
This book shows you how to use Swift 2 to learn about 23 Gang of Four (GoF) design patterns, and is organized into three categories. The book will present you the five creational patterns, followed by the seven structural patterns, and finishing with the 11 behavioral patterns as defined by the GoF. Each chapter will introduce the pattern by defining its role, which common problems the pattern should be used for, its generic UML representation, how each objects presented in the class diagram participate in the pattern, and what the role of each of these objects is. The book then presents you with a concrete case as an illustration that will be used to implement the pattern using Swift.
||Predictive Analytics with Microsoft Azure Machine Learning, 2nd Edition|
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.
The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services.
Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft.
||Learning to Love Data Science|
Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you'll appreciate how data science is fundamentally altering our world, for better and for worse.
Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you'll find out how far data science reaches.