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Machine learning - Mathematics.
概要
作品:
22 作品在 15 項出版品 15 種語言
書目資訊
Mathematics for machine learning /
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Optimization in machine learning and applications /
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A computational approach to statistical learning /
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Evolutionary machine learning techniquesalgorithms and applications /
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Optimization in machine learning and applications
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Deep learning architecturesa mathematical approach /
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Mathematical theories of machine learningtheory and applications /
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Linear algebra and optimization for machine learninga textbook /
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Accelerated optimization for machine learningfirst-order algorithms /
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General-purpose optimization through information maximization
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Linear algebra and optimization with applications to machine learning /
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Black box optimization, machine learning, and no-free lunch theorems
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Mathematical foundations for data analysis
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Algorithmic aspects of machine learning
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Kernel methods for machine learning with Math and R100 exercises for building logic /
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Kernel methods for machine learning with Math and Python100 exercises for building logic /
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Alternating direction method of multipliers for machine learning
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Probabilistic numericscomputation as machine learning /
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Random matrix methods for machine learning
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Essential math for data science :take control of your data with fundamental linear algebra, probability, and statistics /
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Elements of dimensionality reduction and manifold learning
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Fundamental mathematical concepts for machine learning in science
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主題
Communications Engineering, Networks.
Theory of Computation.
Computer algorithms.
Visualization.
Data Science.
Matrix analytic methods.
Information Systems and Communication Service.
Mathematics of Computing.
Data mining
Bioinformatics.
Computational Physics and Simulations.
Estimation theory.
Algorithm Analysis and Problem Complexity.
Mathematical Applications in Computer Science.
Algebras, Linear.
Kernel functions.
Python (Computer program language)
Information Storage and Retrieval.
R (Computer program language)
Dimension reduction (Statistics)
Computational and Systems Biology.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Linear and Multilinear Algebras, Matrix Theory.
Machine learning
Artificial Intelligence.
Machine Learning.
Optimization.
Computational Mathematics and Numerical Analysis.
Probabilities.
Computer science
Math Applications in Computer Science.
Functional analysis.
Mathematical statistics.
Mathematical Models of Cognitive Processes and Neural Networks.
Big Data/Analytics.
Mathematical optimization.
Statistical Learning.
Biological and Physical Engineering.
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