Hands-on mathematics for deep learning : build a solid mathematical foundation for training efficient deep neural networks

Summary: The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you will understand the relevant mathematics that goes behind building deep learning models.

Record details

ISBN: 9781838647292
ISBN: 183864184X
ISBN: 9781838641849
Physical Description: 1 online resource
remote
Publisher: Birmingham : Packt Publishing, 2020.

Content descriptions

Formatted Contents Note:

Non-convex optimization -- Exploring the various optimization methods -- Least squares -- Lagrange multipliers -- Newton's method -- The secant method -- The quasi-Newton method -- Game theory -- Descent methods -- Gradient descent -- Stochastic gradient descent -- Loss functions -- Gradient descent with momentum -- The Nesterov's accelerated gradient -- Adaptive gradient descent -- Simulated annealing -- Natural evolution -- Exploring population methods -- Genetic algorithms -- Particle swarm optimization -- Summary -- Graph Theory -- Understanding the basic concepts and terminology