Bemærk: Kan ikke leveres før jul.
Bemærk: Kan ikke leveres før jul.
This book presents a comprehensive review of heterogeneous face analysis and synthesis, ranging from the theoretical and technical foundations to various hot and emerging applications, such as cosmetic transfer, cross-spectral hallucination and face rotation.
Bemærk: Kan ikke leveres før jul.
Bemærk: Kan ikke leveres før jul.
Contingent Computation offers a new theoretical perspective through which we can engage philosophically with computing. The book proves that aesthetics is a viable mode of investigating contemporary computational systems.
Bemærk: Kan leveres før jul.
Bemærk: Kan leveres før jul.
Key Features:• Build neural networks that can see and understand images• Build an A.I. that will learn to defeat you in a classic Atari game• Hands-on Learning Written for readers... Læs mere
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Key Features: · Example-rich guide · Step-by-step guide · Move from single-machine to massive cluster Readers should have intermediate skills in Java or Scala. No previous machine learning experience is required.
Bemærk: Kan ikke leveres før jul.
Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is... Læs mere
Bemærk: Kan ikke leveres før jul.
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
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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.
Bemærk: Kan leveres før jul.
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry.
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This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable