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Probabilistic Deep Learning

Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty;

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Probabilistic Machine Learning

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian;

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Machine Learning for Speaker Recognition

learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and;

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Deep Learning

, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models;

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Probabilistic and Biologically Inspired Feature Representations

Under the title Probabilistic and Biologically Inspired Feature Representations, this text collects a substantial amount of work on the;

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Probabilistic and Biologically Inspired Feature Representations

Under the title Probabilistic and Biologically Inspired Feature Representations, this text collects a substantial amount of work on the;

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Artificial Intelligence

coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic;

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Probabilistic Graphical Models

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical;

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Probabilistic Graphical Models

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical;

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Intelligent Data Engineering and Automated Learning - IDEAL 2016

This book constitutes the refereed proceedings of the 17 International Conference on Intelligent Data Engineering and Automated Learning;

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Machine Learning

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled;

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Intelligent Data Engineering and Automated Learning - IDEAL 2017

techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling;

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The Probability Companion for Engineering and Computer Science

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic;

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The Probability Companion for Engineering and Computer Science

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic;

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Data Mining and Machine Learning

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze;

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Hands-On One-shot Learning with Python

introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to;

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Machine Learning and Knowledge Discovery in Databases

and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches;

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Machine Learning and Knowledge Discovery in Databases

and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches;

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Machine Learning and Knowledge Discovery in Databases

; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical;

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Hydraulic Structures

Hydraulic Structures: Probabilistic Approaches to Maintenance is an introduction to the field of probability theory and its applications;

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Artificial Intelligence with Python Cookbook

probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text;

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Computational Learning And Probabilistic Reasoning

techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational;

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Advances in Independent Component Analysis and Learning Machines

theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.

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Machine Learning and Knowledge Discovery in Databases

graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods;

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Machine Learning and Knowledge Discovery in Databases

graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods;

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Machine Learning and Knowledge Discovery in Databases

networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation;

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Machine Learning and Knowledge Discovery in Databases

; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and;

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