Réseaux de Croyance Profonde

Réseaux de Croyance Profonde Séries

Cette série plonge dans le domaine de pointe de l'intelligence artificielle, explorant des architectures de réseaux neuronaux inspirées par la structure du cerveau humain. Elle examine des modèles élégants capables d'apprendre des concepts abstraits à partir de composants plus simples, reflétant la cognition humaine. Les lecteurs découvriront les éléments constitutifs essentiels de ces systèmes sophistiqués, des concepts fondamentaux aux techniques avancées pour le traitement d'images et de séries temporelles. L'œuvre offre une plongée profonde dans les principes de ces puissants algorithmes d'apprentissage.

Ordre de lecture recommandé

Deep Belief Nets in C++ and CUDA C: Volume 1

Timothy Masters

219pages
8 heures de lecture

Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you’ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will Learn Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.