Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.Key FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize you...

Augmented Reality (AR) blurs the boundary between the physical and digital worlds. In AR’s current exploration phase, innovators are beginning to create compelling and contextually rich applications that enhance a user’s everyday experiences. In this book, Dr. Helen Papagiannis—a world-leading expert in the field—introduces you to AR: how it’s evolving, where the opportunities are, and where it’s headed.If you’re a designer, developer, entrepreneur, student, educator, business lead...

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 in images, understanding text, analyzing video, and predicting the properties of potential medicines.TensorFlow for Deep Learning teaches concepts ...

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data.The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to eas...

Get to grips with the essentials of deep learning by leveraging the power of PythonKey FeaturesYour one-stop solution to get started with the essentials of deep learning and neural network modelingTrain different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and moreCovers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deployin...

Powerful, independent recipes to build deep learning models in different application areas using R librariesKey FeaturesMaster intricacies of R deep learning packages such as mxnet & tensorflowLearn application on deep learning in different domains using practical examples from text, image and speechGuide to set-up deep learning models using CPU and GPUBook DescriptionDeep Learning is the next big thing. It is a part of machi...

Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than bli...

Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existenceKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsImplement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learningA wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANsBook DescriptionRecent develop...

Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using PythonKey FeaturesA go-to guide to help you master AI algorithms and concepts8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillanceUse TensorFlow, Keras, and other Python libraries to implement smart AI applicationsBook DescriptionThis book will be a perfect companion if you want to b...

This book addresses the interests of a large community of researchers in the fields of XML-based annotation techniques and corpus-based language technology. It covers the most significant recent developments in this field, from multi-layered mark-up and standards to theoretical formalisms to applications. The contributions are based on research projects at international level in text technology, computational linguistics, hypertext modeling and in the domain of standards and tools for lang...

Build simple, maintainable, and easy to deploy machine learning applications.Key Features\[\*\] Build simple, but powerful, machine learning applications that leverage Go’s standard library along with popular Go packages.\[\*\] Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go\[\*\] Understand when and how to integrate certain types of machine learning model in Go applications.

A Journey Through Cultures addresses one of the hottest topics in contemporary HCI: cultural diversity amongst users. For a number of years the HCI community has been investigating alternatives to enhance the design of cross-cultural systems. Most contributions to date have followed either a ‘design for each’ or a ‘design for all’ strategy.A Journey Through Cultures takes a very different approach. Proponents of CVM – the Cultural Viewpoint Metaphors perspective –...