This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks.
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition-as well as some we don´t yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as ´´Big Data´´ has gotten bigger, the theory of machine learning-the foundation of efforts to process that data into knowledge-has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.
Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages. Requiring no previous experience, this book is for the true programming beginner. It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization. Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve. A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study. A friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages No previous experience required-this book is for the true programming beginner! Step-by-step examples, thorough explanations, hands-on exercises, and sample code supports your learning curve
Gain hands-on experience with SPARQL, the RDF query language that?s bringing new possibilities to semantic web, linked data, and big data projects. This updated and expanded edition shows you how to use SPARQL 1.1 with a variety of tools to retrieve, manipulate, and federate data from the public web as well as from private sources. Author Bob DuCharme has you writing simple queries right away before providing background on how SPARQL fits into RDF technologies. Using short examples that you can run yourself with open source software, you?ll learn how to update, add to, and delete data in RDF datasets. * Get the big picture on RDF, linked data, and the semantic web * Use SPARQL to find bad data and create new data from existing data * Use datatype metadata and functions in your queries * Learn techniques and tools to help your queries run more efficiently * Use RDF Schemas and OWL ontologies to extend the power of your queries * Discover the roles that SPARQL can play in your applications
Agile revolutionized the way people think about developing software, but there are literally dozens of ways that you can ´´go agile.´´ While one team may find a particular agile practice easy to use, another team may find the same practice devilishly difficult. This book demystifies agile methodologies: why they´re designed the way they are, what problems they address, and the values, principles, and ideas they embody. Learning Agile helps you recognize the principles that apply to development problems specific to your team, company, and projects. You´ll discover how to use that information to guide your choice of methodologies and practices. With this book you´ll learn: Values that effective software teams possess The methodologies that embody those values The practices that make up those methodologies And principles that help you bring those values, methodologies, and practices to your team and your company
Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field´s intellectual foundations to the most recent developments and applications.