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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...
Quantum Variational Calculus
Quantum Variational Calculus

This Brief puts together two subjects, quantum and variational calculi by considering variational problems involving Hahn quantum operators. The main advantage of its results is that they are able to deal with nondifferentiable (even discontinuous) functions, which are important in applications.

Possible applications in...

Introduction to Hilbert Spaces with Applications
Introduction to Hilbert Spaces with Applications
The Second Edition if this successful text offers a systematic exposition of the basic ideas and results of Hilbert space theory and functional analysis. It includes a simple introduction to the Lebesgue integral and a new chapter on wavelets. The book provides the reader with revised examples and updated diverse applications to differential and...
Design Sensitivity Analysis and Optimization of Electromagnetic Systems (Mathematical and Analytical Techniques with Applications to Engineering)
Design Sensitivity Analysis and Optimization of Electromagnetic Systems (Mathematical and Analytical Techniques with Applications to Engineering)

This book presents a comprehensive introduction to design sensitivity analysis theory as applied to electromagnetic systems. It treats the subject in a unified manner, providing numerical methods and design examples. The specific focus is on continuum design sensitivity analysis, which offers significant advantages over discrete...

R Deep Learning Projects: Master the techniques to design and develop neural network models in R
R Deep Learning Projects: Master the techniques to design and develop neural network models in R

5 real-world projects to help you master deep learning concepts

Key Features

  • Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more
  • Get to grips with R's impressive range of...
Mathematical Principles of Optical Fiber Communication (CBMS-NSF Regional Conference Series in Applied Mathematics)
Mathematical Principles of Optical Fiber Communication (CBMS-NSF Regional Conference Series in Applied Mathematics)
With the maturing of mobile portable telephony and the emerging broadband access market, greater fiber transmission capacity will be essential in the early 21st century. Since the demand for more capacity drives the development of new optics-based technologies, fiber optics therefore remains a vibrant area for research. Mathematical Principles of...
Path Integrals in Physics Volume 2: Quantum Field Theory, Statistical Physics & Other Modern Applications
Path Integrals in Physics Volume 2: Quantum Field Theory, Statistical Physics & Other Modern Applications
This book covers the fundamentals of path integrals, both the Wiener and Feynman types, and their many applications in physics. It deals with systems that have an infinite number of degrees of freedom. The book discusses the general physical background and concepts of the path integral approach used, followed by the most typical and important...
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...

The Finite Element Method: Theory, Implementation, and Applications (Texts in Computational Science and Engineering)
The Finite Element Method: Theory, Implementation, and Applications (Texts in Computational Science and Engineering)

This book gives an introduction to the finite element method as a general computational method for solving partial differential equations approximately. Our approach is mathematical in nature with a strong focus on the underlying mathematical principles, such as approximation properties of piecewise polynomial spaces, and variational...

Fixed-Point Algorithms for Inverse Problems in Science and Engineering (Springer Optimization and Its Applications)
Fixed-Point Algorithms for Inverse Problems in Science and Engineering (Springer Optimization and Its Applications)

"Fixed-Point Algorithms for Inverse Problems in Science and Engineering" presents some of the most recent work from top-notch researchers studying projection and other first-order fixed-point algorithms in several areas of mathematics and the applied sciences. The material presented provides a survey of the state-of-the-art...

An Introduction to Partial Differential Equations
An Introduction to Partial Differential Equations
"This is an introductory book on the subject of partial differential equations which is suitable for a large variety of basic courses on this topic. In particular, it can be used as a textbook or self-study book for large classes of readers with interests in mathematics, engineering, and related fields. Its usefulness stems from its clarity,...
Computer Vision Analysis of Image Motion by Variational Methods (Springer Topics in Signal Processing)
Computer Vision Analysis of Image Motion by Variational Methods (Springer Topics in Signal Processing)

This book presents a unified view of image motion analysis under the variational framework. Variational methods, rooted in physics and mechanics, but appearing in many other domains, such as statistics, control, and computer vision, address a problem from an optimization standpoint, i.e., they formulate it as the optimization of an objective...

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