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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...
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...
Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

A comprehensive guide to advanced deep learning techniques, including autoencoders, GANs, VAEs, and deep reinforcement learning that drive today's most impressive AI results

Key Features

  • Explore the most advanced deep learning techniques that drive modern AI results
  • ...
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...

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...

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,...
Partial Differential Equations: Modelling and Numerical Simulation (Computational Methods in Applied Sciences)
Partial Differential Equations: Modelling and Numerical Simulation (Computational Methods in Applied Sciences)
This book is dedicated to Olivier Pironneau.

For more than 250 years partial differential equations have been clearly the most important tool available to mankind in order to understand a large variety of phenomena, natural at first and then those originating from human activity and technological development. Mechanics, physics and their...

Applied Calculus of Variations for Engineers
Applied Calculus of Variations for Engineers

The subject of calculus of variations is to find optimal solutions to engineering problems where the optimum may be a certain quantity, a shape, or a function. Applied Calculus of Variations for Engineers addresses this very important mathematical area applicable to many engineering disciplines. Its unique,...

Probability, Statistical Optics and Data Testing (Springer Series in Information Sciences)
Probability, Statistical Optics and Data Testing (Springer Series in Information Sciences)
Scientists in optics are increasingly confronted with problems that are of a random nature and that require a working knowledge of probability and statistics for their solution. This textbook develops these subjects within the context of optics using a problem-solving approach. All methods are explicitly derived and can be traced back to three...
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...

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