Math and Architectures of Deep Learning
Discover what's going on inside the black box! To work with deep learning you'll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systema...
| Autor principal: | |
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| Otros Autores: | , , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
New York :
Manning Publications Co. LLC
2024.
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| Edición: | 1st ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009819037106719 |
Tabla de Contenidos:
- An overview of machine learning and deep learning
- Vectors, matrices, and tensors in machine learning
- Classifiers and vector calculus
- Linear algebraic tools in machine learning
- Probability distributions in machine learning
- Bayesian tools for machine learning
- Function approximation : how neural networks model the world
- Training neural networks : forward propagation and backpropagation
- Loss, optimization, and regularization
- Convolutions in neural networks
- Neural networks for image classification and object detection
- Manifolds, homeomorphism, and neural networks
- Fully Bayes model parameter estimation
- Latent space and generative modeling, autoencoders, and variational autoencoders.
