A handbook of mathematical models with Python : elevate your machine learning projects with NetworkX, PuLP, and Linalg

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...

Descripción completa

Detalles Bibliográficos
Otros Autores: Sarkar, Ranja, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd 2023.
Edición:1st edition
Materias:
Machine learning > Mathematical models.
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009764836906719
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.

Ejemplares similares