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Hardware-Aware Probabilistic Machine Learning Models

This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models;

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Thinking Machines

Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic;

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth;

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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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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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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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Learning Probabilistic Graphical Models in R

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book;

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Handbook of Probabilistic Models

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields;

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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

selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and;

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OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

the Second International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with MICCAI 2019, in Shenzhen;

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Deep Learning Systems

This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep;

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Computational Trust Models and Machine Learning

Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various;

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Foundations of Data Science

algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including;

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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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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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Graphical Models for Machine Learning and Digital Communication

learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he;

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A Brief Introduction to Machine Learning for Engineers

introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning;

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Machine Learning for Finance

products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What;

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Machine Learning in Bioinformatics

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are;

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Mastering Machine Learning Algorithms

machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage;

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning

submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics;

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Caffe2 Quick Start Guide

with other deep learning frameworks on Caffe2 Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devices;

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Thoughtful Machine Learning

Learn how to apply test-driven development (TDD) to machine-learning algorithms - and catch mistakes that could sink your analysis. In this;

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Data-Driven Computational Neuroscience

. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning;

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Reasoning with Probabilistic and Deterministic Graphical Models

many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and;

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Reasoning with Probabilistic and Deterministic Graphical Models

many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and;

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