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Linear Algebra and Optimization for Machine Learning

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout;

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Linear Algebra and Optimization for Machine Learning

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout;

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Linear Algebra and Optimization with Applications to Machine Learning - Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied;

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Mathematics for Machine Learning

central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For;

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Mathematics for Machine Learning

central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For;

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Introduction To Linear Algebra

Features Includes cutting edge applications in machine learning and data analytics. Suitable as a primary text for;

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Math for Programmers

* Core concepts from linear algebra * Calculus with one or more variables * Algorithms for regression, classification, and clustering;

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Linear and Integer Optimization

mathematical proofs, linear algebra, graph theory, convexity, and nonlinear optimization. All chapters contain extensive examples and exercises. This;

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TensorFlow for Deep Learning From Linear Regression to Reinforcement Learning

you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how;

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Machine Learning

theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as;

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Probabilistic Machine Learning

optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics;

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Machine Learning: A Practical Approach on the Statistical Learning Theory

theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as;

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Challenges and Strategies in Teaching Linear Algebra

pedagogy of linear algebra with a particular emphasis on tasks that are productive for learning. The main themes addressed include;

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Finite-Dimensional Linear Algebra

Linear algebra forms the basis for much of modern mathematics-theoretical, applied, and computational. Finite-Dimensional Linear Algebra;

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Accelerated Optimization for Machine Learning

provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It;

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Accelerated Optimization for Machine Learning

provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It;

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Numerical Algorithms

needed to support these skills. The book covers a wide range of topics-from numerical linear algebra to optimization and differential;

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Machine Learning with Scala Quick Start Guide

concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data;

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Deep Learning with Python

. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having;

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Deep Learning with Python

problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization;

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Introduction to Machine Learning

network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and;

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Introduction to Linear Algebra

learning and analysing linear mapping. Linear algebra is also important in understanding differential equations, abstract algebra, functional;

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Optimization for Machine Learning

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both;

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A Brief Introduction to Machine Learning for Engineers

to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.;

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Alternating Direction Method of Multipliers for Machine Learning

linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on;

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Introduction to Applied Linear Algebra

teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least;

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Distributed Machine Learning and Gradient Optimization

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the;

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