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書目資訊
主題
Machine learning - Mathematics.
概要
作品:
17 作品在 15 項出版品 15 種語言
書目資訊
Mathematical theories of machine learning = theory and applications /
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Evolutionary machine learning techniques = algorithms and applications /
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Optimization in machine learning and applications
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Black box optimization, machine learning, and no-free lunch theorems
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Deep learning architectures = a mathematical approach /
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Mathematical foundations for data analysis
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Algorithmic aspects of machine learning
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Linear algebra and optimization for machine learning = a textbook /
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Accelerated optimization for machine learning = first-order algorithms /
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Kernel methods for machine learning with Math and R = 100 exercises for building logic /
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Kernel methods for machine learning with Math and Python = 100 exercises for building logic /
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Probabilistic numerics = computation as machine learning /
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Random matrix methods for machine learning
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Fundamental mathematical concepts for machine learning in science
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Alternating direction method of multipliers for machine learning
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General-purpose optimization through information maximization
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Elements of dimensionality reduction and manifold learning
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主題
Communications Engineering, Networks.
Visualization.
Computer algorithms.
Theory of Computation.
Data Science.
Matrix analytic methods.
Information Systems and Communication Service.
Mathematics of Computing.
Bioinformatics.
Computational Physics and Simulations.
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.
Data Mining and Knowledge Discovery.
Machine learning- Mathematics.
Computational Intelligence.
Linear and Multilinear Algebras, Matrix Theory.
Artificial Intelligence.
Machine Learning.
Optimization.
Computational Mathematics and Numerical Analysis.
Data mining- Mathematics.
Math Applications in Computer Science.
Functional analysis.
Machine learning- Statistical methods.
Big Data/Analytics.
Mathematical Models of Cognitive Processes and Neural Networks.
Mathematical optimization.
Statistical Learning.
Biological and Physical Engineering.
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