A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON elevate your machine learning projects with Networkx, PuLP, and linalg /

Master the art of mathematical modeling through practical examples, use cases, and machine learning techniques Key Features Gain a profound understanding of various mathematical models that can be integrated with machine learning Learn how to implement optimization algorithms to tune machine learnin...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Sarkar, Ranja (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:1st edition.
Temas:
Machine learning > Mathematical models.
Python (Computer program language)
Electronic books.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Preface
  • Part 1: Mathematical Modeling
  • 1
  • Introduction to Mathematical Modeling
  • Mathematical optimization
  • Understanding the problem
  • Formulation of the problem
  • Signal processing
  • Understanding the problem
  • Formulation of the problem
  • Control theory
  • Understanding the problem
  • Formulation of the problem
  • Summary
  • 2
  • Machine Learning vis-à-vis Mathematical Modeling
  • ML as mathematical optimization
  • Example 1
  • regression
  • Example 2
  • neural network
  • ML
  • a predictive tool
  • E-commerce
  • Sales and marketing
  • Cybersecurity
  • Mathematical modeling
  • a prescriptive tool
  • Finance
  • Retail
  • Energy
  • Digital advertising
  • Summary
  • Part 2: Mathematical Tools
  • 3
  • Principal Component Analysis
  • Linear algebra for PCA
  • Covariance matrix
  • eigenvalues and eigenvectors
  • Number of PCs
  • how to select for a dataset
  • Feature extraction methods
  • LDA
  • the difference from PCA
  • Applications of PCA
  • Noise reduction
  • Anomaly detection
  • Summary
  • 4
  • Gradient Descent
  • Gradient descent variants
  • Application of gradient descent
  • Mini-batch gradient descent and stochastic gradient descent
  • Gradient descent optimizers
  • Momentum
  • Adagrad
  • RMSprop
  • Adam
  • Summary
  • 5
  • Support Vector Machine
  • Support vectors in SVM
  • Kernels for SVM
  • Implementation of SVM
  • Summary
  • 6
  • Graph Theory
  • Types of graphs
  • Undirected graphs
  • Directed graphs
  • Weighted graphs
  • Optimization use case
  • Optimization problem
  • Optimized solution
  • Graph neural networks
  • Summary
  • 7
  • Kalman Filter
  • Computation of measurements
  • Filtration of measurements
  • Implementation of the Kalman filter
  • Summary
  • 8
  • Markov Chain
  • Discrete-time Markov chain
  • Transition probability
  • Application of the Markov chain
  • Markov Chain Monte Carlo
  • Gibbs sampling algorithm
  • Metropolis-Hastings algorithm
  • Illustration of MCMC algorithms
  • Summary
  • Part 3: Mathematical Optimization
  • 9
  • Exploring Optimization Techniques
  • Optimizing machine learning models
  • Random search
  • Grid search
  • Bayesian optimization
  • Optimization in operations research
  • Evolutionary optimization
  • Summary
  • 10
  • Optimization Techniques for Machine Learning
  • General optimization algorithms
  • First-order algorithms
  • Second-order algorithms
  • Complex optimization algorithms
  • Differentiability of objective functions
  • Direct and stochastic algorithms
  • Summary
  • Epilogue
  • Index
  • Other Books You May Enjoy

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