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Algorithmic Learning in a Random World

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to;

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Algorithmic Learning in a Random World

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to;

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Algorithmic Learning in a Random World

of machine learning. The book covers both key conformal predictors and the mathematical analysis of their properties. Algorithmic Learning in a;

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Algorithmic Techniques for the Polymer Sciences

This new book-the first of its kind-examines the use of algorithmic techniques to compress random and non-random sequential strings found;

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Algorithmic Techniques for the Polymer Sciences

This new book-the first of its kind-examines the use of algorithmic techniques to compress random and non-random sequential strings found;

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Foundations of Data Science

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high;

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Aspects of Kolmogorov Complexity the Physics of Information

Martin-Lof level of randomness as found in random sequential binary strings. A classical study of statistics that addresses both a fundamental;

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Algorithmic Learning Theory

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB;

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Intelligent Random Walk

This book examines the intelligent random walk algorithms based on learning automata: these versions of random walk algorithms gradually;

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Learn Algorithmic Trading

Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading;

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Python for Algorithmic Trading From Idea to Cloud Deployment

deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for;

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Algorithmic Foundations of Robotics XIII: Proceedings of the 13th Workshop on the Algorithmic Foundations of Robotics

fundamental algorithmic robotics (e.g., planning, learning, navigation, control, manipulation, optimality, completeness, and complexity) demonstrated;

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Algorithmic Aspects in Information and Management

submissions. The papers deal with emerging important algorithmic problems with a focus on the fundamental background, theoretical technology;

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Data Science Algorithms in a Week

Build a strong foundation of machine learning algorithms in 7 days Key Features Use Python and its wide array of machine learning libraries;

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Algorithmic Foundations of Robotics XIII

fundamental algorithmic robotics (e.g., planning, learning, navigation, control, manipulation, optimality, completeness, and complexity) demonstrated;

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Algorithmic Learning Theory

This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari;

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Random Graphs for Statistical Pattern Recognition

resource presents: a detailed look at the application of random graphs to pattern recognition; extensive examples of applications of the graphs;

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Algorithmic Aspects of Machine Learning

be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method;

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Algorithmic Aspects of Machine Learning

be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method;

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Randomness and Elements of Decision Theory Applied to Signals

professionals who need a quick introduction but practical introduction how to deal with random signals and processes;

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Generative Art

principles of algorithmic art * Brief Processing language tutorial * Establishing artistic ground rules for a new work * Using random, semi;

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Data Science Projects with Python

Key Features Think critically about data and use it to form and test a hypothesis Choose an appropriate machine learning model and train;

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The Essentials of Machine Learning in Finance and Accounting

such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and;

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The Essentials of Machine Learning in Finance and Accounting

such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and;

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Understanding Machine Learning

introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the;

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