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Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition
Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features

  • Build a strong foundation in neural networks and deep learning with Python libraries
  • Explore advanced deep learning techniques and their applications...
Statistical Performance Analysis and Modeling Techniques for Nanometer VLSI Designs
Statistical Performance Analysis and Modeling Techniques for Nanometer VLSI Designs
As VLSI technology scales into nanometer regime, chip design engineering faces several challenges. One profound change in the chip design business is that engineers cannot realize the design precisely into the silicon chips. Chip performance, manufacture yield, and lifetime thereby cannot be determined accurately at the design...
Convex Analysis and Nonlinear Optimization: Theory and Examples (CMS Books in Mathematics)
Convex Analysis and Nonlinear Optimization: Theory and Examples (CMS Books in Mathematics)
MATHEMATICAL REVIEWS

"The present book gives a concise treatment of the area, aiming to show the relevance in particular of new developments in nonsmooth analysis to optimization theory…The book is of a manageable size and as such should appeal to the student. Further, the proofs are generally short and snappy, revealing the power...

Families of Conformally Covariant Differential Operators, Q-Curvature and Holography (Progress in Mathematics)
Families of Conformally Covariant Differential Operators, Q-Curvature and Holography (Progress in Mathematics)
The central object of the book is Q-curvature. This important and subtle scalar Riemannian curvature quantity was introduced by Tom Branson about 15 year ago in connection with variational formulas for determinants of conformally covariant differential operators. The book studies structural properties of Q-curvature from an extrinsic point of view...
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications

This book presents the most recent achievements in data assimilation in Geosciences, especially in regards to meteorology, oceanography and hydrology. It spans both theoretical and applied aspects with various methodologies including variational, Kalman filter, maximum likelihood ensemble filter and other ensemble methods. Besides data...

Micromechanics of Fracture in Generalized Spaces
Micromechanics of Fracture in Generalized Spaces

By the detailed analysis of the modern development of the mechanics of deformable media can be found the deep internal contradiction. From the one hand it is declared that the deformation and fracture are the hierarchical processes which are linked and unite several structural and scale levels. From the other hand the sequential investigation of...

Super-Resolution Imaging (Digital Imaging and Computer Vision)
Super-Resolution Imaging (Digital Imaging and Computer Vision)

This book functions as the definitive overview of the field of super-resolution imaging. Written by the leading researchers in the field of image and video super-resolution, it surveys the latest state-of-the-art techniques in super-resolution imaging. Each detailed chapter provides coverage of the implementations and applications of...

Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities

Quadratic programming (QP) is one advanced mathematical technique that allows for the optimization of a quadratic function in several variables in the presence of linear constraints. This book presents recently developed algorithms for solving large QP problems and focuses on algorithms which are, in a sense optimal, i.e., they can solve...

Energy Minimization Methods in Computer Vision and Pattern Recognition: 8th International Conference, EMMCVPR 2011
Energy Minimization Methods in Computer Vision and Pattern Recognition: 8th International Conference, EMMCVPR 2011

Over the last few decades, energy minimization methods have become an established paradigm to resolve a variety of challenges in the fields of computer vision and pattern recognition. While traditional approaches to computer vision were often based on a heuristic sequence of processing steps and merely allowed a very limited...

Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning)
Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning)

A variety of problems m machine learning and digital communication deal with complex but structured natural or artificial systems. Natural patterns mat we wish to automatically classify' are a consequence of a hierarchical causal physical process. Learning about the world m which we live requires mat we extract useful sensor)'...

Mathematical Problems in Image Processing
Mathematical Problems in Image Processing
It is surprising when we realize just how much we are surrounded by images. Images allow us not only to perform complex tasks on a daily basis, but also to communicate, transmit information, represent and understand the world around us. Just think, for instance about digital television, medical imagery, video-surveillance, etc....
Multiscale Theory of Composites and Random Media
Multiscale Theory of Composites and Random Media

This is the first book to introduce Green-function-based multiscale theory and the corresponding finite element method, which are readily applicable to composites and random media. The methodology is considered to be the one that most effectively tackles the uncertainty of stress propagation in complex heterogeneities of random...

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