Mathematics Essentials for Machine Learning
portes grátis
Mathematics Essentials for Machine Learning
Build a solid mathematical foundation for a career in machine learning
Semenski, Terezija
Packt Publishing Limited
10/2024
Mole
Inglês
9781835088432
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Table of Contents
Building a strong foundation in Linear Algebra
Navigating Vectors and Matrices
Implementing Gaussian elimination
Transforming matrices from orthogonality to Gram-Schmidt process
Unveiling Eigenvalues and Eigenvectors
Providing an introduction to Calculus
Applying the power of derivatives and differentiation
Extending to multivariate Calculus and ML gradients
Identifying key components of integral Calculus
Creating key foundations of Probability and the probability rules
Examining fundamental properties of discrete probability distributions
Investigating essential properties of continuous probability distributions
Exploiting the power of the Bayes' Theorem
Introducing Statistics and the summary statistics
Calculating quantiles and correlation
Working with random variables and probability distribution
Implementing sampling and replacement
Applying linear regression
Building a strong foundation in Linear Algebra
Navigating Vectors and Matrices
Implementing Gaussian elimination
Transforming matrices from orthogonality to Gram-Schmidt process
Unveiling Eigenvalues and Eigenvectors
Providing an introduction to Calculus
Applying the power of derivatives and differentiation
Extending to multivariate Calculus and ML gradients
Identifying key components of integral Calculus
Creating key foundations of Probability and the probability rules
Examining fundamental properties of discrete probability distributions
Investigating essential properties of continuous probability distributions
Exploiting the power of the Bayes' Theorem
Introducing Statistics and the summary statistics
Calculating quantiles and correlation
Working with random variables and probability distribution
Implementing sampling and replacement
Applying linear regression
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Table of Contents
Building a strong foundation in Linear Algebra
Navigating Vectors and Matrices
Implementing Gaussian elimination
Transforming matrices from orthogonality to Gram-Schmidt process
Unveiling Eigenvalues and Eigenvectors
Providing an introduction to Calculus
Applying the power of derivatives and differentiation
Extending to multivariate Calculus and ML gradients
Identifying key components of integral Calculus
Creating key foundations of Probability and the probability rules
Examining fundamental properties of discrete probability distributions
Investigating essential properties of continuous probability distributions
Exploiting the power of the Bayes' Theorem
Introducing Statistics and the summary statistics
Calculating quantiles and correlation
Working with random variables and probability distribution
Implementing sampling and replacement
Applying linear regression
Building a strong foundation in Linear Algebra
Navigating Vectors and Matrices
Implementing Gaussian elimination
Transforming matrices from orthogonality to Gram-Schmidt process
Unveiling Eigenvalues and Eigenvectors
Providing an introduction to Calculus
Applying the power of derivatives and differentiation
Extending to multivariate Calculus and ML gradients
Identifying key components of integral Calculus
Creating key foundations of Probability and the probability rules
Examining fundamental properties of discrete probability distributions
Investigating essential properties of continuous probability distributions
Exploiting the power of the Bayes' Theorem
Introducing Statistics and the summary statistics
Calculating quantiles and correlation
Working with random variables and probability distribution
Implementing sampling and replacement
Applying linear regression
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
