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Machine Learning Mathematics

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Machine Learning Mathematics

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Machine Learning Mathematics

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Mathematics for Machine Learning

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions;

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Mathematics for Machine Learning

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions;

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Quantum Machine Learning with Python

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use;

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Machine Learning for Speaker Recognition

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition;

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Machine Learning

AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS PROSE Award Finalist 2019;

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Mathematics and Programming for Machine Learning with R

Based on the author's experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R;

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Mathematics and Programming for Machine Learning with R

Based on the author's experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R;

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Metaheuristics in Machine Learning

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered;

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Statistical and Machine Learning Approaches for Network Analysis

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches;

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Machine Learning

concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine;

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Machine Learning

concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine;

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Signal Processing and Machine Learning with Applications

Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with;

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Understanding Machine Learning

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to;

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The Mathematics of Data

science, as well as many other areas. This book gives an introduction to the mathematical methods that form the foundations of machine learning;

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Phase Transitions In Machine Learning

spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin;

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Machine Learning and its Applications

In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large;

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Machine Learning and its Applications

In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large;

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Quantum Machine Learning with Python

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use;

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Introduction To Machine Learning In Quantitative Finance, An

In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all;

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Introduction To Machine Learning In Quantitative Finance, An

In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all;

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Dynamic Fuzzy Machine Learning

Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field;

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Practical Machine Learning Cookbook

Resolving and offering solutions to your machine learning problems with R About This Book * Implement a wide range of algorithms and;

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Mathematics for Future Computing and Communications

, Bayesian inference, machine learning, reinforcement learning and many other areas.;

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Advanced Computing in Industrial Mathematics

modeling, machine learning, digitization and imaging, advanced computing in environmental, biomedical and engineering applications.;

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