Fundamentals of deep learning : designing next-generation machine intelligence algorithms /
Nithin Buduma, Nikhil Buduma, and Joe Papa ; with contributions by Nicholas Locascio.
- Second edition.
- xiii, 372 páginas ilustraciones
Previous edition: published as by Nikhil Buduma with contributions by Nicholas Locascio. 2017.
Fundamentals of linear algebra for deep learning -- Fundamentals of probability -- The neural network -- Training feed-forward neural networks -- Implementing neural networks in PyTorch -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Generative models -- Methods in interpretability -- Memory augmented neural networks -- Deep reinforcement learning.
We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity.
9781492082187
INTELIGENCIA ARTIFICIAL APRENDIZAJE AUTOMATICO Neural networks (Computer science) REDES NEURONALES (ciencia de la computación)