Machine Learning
portes grátis
Machine Learning
From the Classics to Deep Networks, Transformers, and Diffusion Models
Theodoridis, Sergios
Elsevier Science Publishing Co Inc
04/2025
1200
Mole
Inglês
9780443292385
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1. Introduction
2. Probability and Stochastic Processes
3. Learning in Parametric Modelling: Basic Concepts and Directions
4. Mean-Square Error Linear Estimation
5. Stochastic Gradient Descent: the LMS Algorithm and its Family
6. The Least-Squares Family
7. Classification: A Tour of the Classics
8. Parameter Learning: A Convex Analytic Path
9. Sparsity-Aware Learning: Concepts and Theoretical Foundations
10. Sparsity-Aware Learning: Algorithms and Applications
11. Learning in Reproducing Kernel Hilbert Spaces
12. Bayesian Learning: Inference and the EM Algorithm
13. Bayesian Learning: Approximate Inference and Nonparametric Models
14. Monte Carlo Methods
15. Probabilistic Graphical Models: Part 1
16. Probabilistic Graphical Models: Part 2
17. Particle Filtering
18. Neural Networks and Deep Learning: Part 1
19. Neural Networks and Deep Learning: Part 2
20. Dimensionality Reduction and Latent Variables Modeling
2. Probability and Stochastic Processes
3. Learning in Parametric Modelling: Basic Concepts and Directions
4. Mean-Square Error Linear Estimation
5. Stochastic Gradient Descent: the LMS Algorithm and its Family
6. The Least-Squares Family
7. Classification: A Tour of the Classics
8. Parameter Learning: A Convex Analytic Path
9. Sparsity-Aware Learning: Concepts and Theoretical Foundations
10. Sparsity-Aware Learning: Algorithms and Applications
11. Learning in Reproducing Kernel Hilbert Spaces
12. Bayesian Learning: Inference and the EM Algorithm
13. Bayesian Learning: Approximate Inference and Nonparametric Models
14. Monte Carlo Methods
15. Probabilistic Graphical Models: Part 1
16. Probabilistic Graphical Models: Part 2
17. Particle Filtering
18. Neural Networks and Deep Learning: Part 1
19. Neural Networks and Deep Learning: Part 2
20. Dimensionality Reduction and Latent Variables Modeling
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial intelligence; Autoencoders; Autonomous cars; Bayesian networks; BERT; Classification; Clustering; Computer vision; Conditional random fields; Cover's theorem; Deep belief networks; Diffusion models; d-separation; Factor graphs; Generative adversarial networks (GANs); GPT; I-maps; Ising and Potts models; Kernel ridge regression; Large language models; Machine learning; Markov condition; Markov random fields; Message passing algorithms for chains and trees; Moralization; Natural language processing; Nonlinear modeling; Positive definite kernels; Probabilistic graphical models; Regression; Reinforcement learning; Representer theorem; Reproducing kernel Hilbert spaces; Robotics; Self-supervised learning; Semisupervised learning; Speech recognition; Supervised learning; The kernel trick; Transformers; Unsupervised learning; Variational autoencoders; Voltera, Wiener and Hammerstein models;
1. Introduction
2. Probability and Stochastic Processes
3. Learning in Parametric Modelling: Basic Concepts and Directions
4. Mean-Square Error Linear Estimation
5. Stochastic Gradient Descent: the LMS Algorithm and its Family
6. The Least-Squares Family
7. Classification: A Tour of the Classics
8. Parameter Learning: A Convex Analytic Path
9. Sparsity-Aware Learning: Concepts and Theoretical Foundations
10. Sparsity-Aware Learning: Algorithms and Applications
11. Learning in Reproducing Kernel Hilbert Spaces
12. Bayesian Learning: Inference and the EM Algorithm
13. Bayesian Learning: Approximate Inference and Nonparametric Models
14. Monte Carlo Methods
15. Probabilistic Graphical Models: Part 1
16. Probabilistic Graphical Models: Part 2
17. Particle Filtering
18. Neural Networks and Deep Learning: Part 1
19. Neural Networks and Deep Learning: Part 2
20. Dimensionality Reduction and Latent Variables Modeling
2. Probability and Stochastic Processes
3. Learning in Parametric Modelling: Basic Concepts and Directions
4. Mean-Square Error Linear Estimation
5. Stochastic Gradient Descent: the LMS Algorithm and its Family
6. The Least-Squares Family
7. Classification: A Tour of the Classics
8. Parameter Learning: A Convex Analytic Path
9. Sparsity-Aware Learning: Concepts and Theoretical Foundations
10. Sparsity-Aware Learning: Algorithms and Applications
11. Learning in Reproducing Kernel Hilbert Spaces
12. Bayesian Learning: Inference and the EM Algorithm
13. Bayesian Learning: Approximate Inference and Nonparametric Models
14. Monte Carlo Methods
15. Probabilistic Graphical Models: Part 1
16. Probabilistic Graphical Models: Part 2
17. Particle Filtering
18. Neural Networks and Deep Learning: Part 1
19. Neural Networks and Deep Learning: Part 2
20. Dimensionality Reduction and Latent Variables Modeling
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial intelligence; Autoencoders; Autonomous cars; Bayesian networks; BERT; Classification; Clustering; Computer vision; Conditional random fields; Cover's theorem; Deep belief networks; Diffusion models; d-separation; Factor graphs; Generative adversarial networks (GANs); GPT; I-maps; Ising and Potts models; Kernel ridge regression; Large language models; Machine learning; Markov condition; Markov random fields; Message passing algorithms for chains and trees; Moralization; Natural language processing; Nonlinear modeling; Positive definite kernels; Probabilistic graphical models; Regression; Reinforcement learning; Representer theorem; Reproducing kernel Hilbert spaces; Robotics; Self-supervised learning; Semisupervised learning; Speech recognition; Supervised learning; The kernel trick; Transformers; Unsupervised learning; Variational autoencoders; Voltera, Wiener and Hammerstein models;
