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Pereyra N: Limits and Derivatives of
Real Functions for Physicists

Limits and Derivatives of Real Functions for Physicists

Selected Works Of Roderick S. C. Wong, The - Volume 4

An accessible resource to help you puzzle out logic concepts Logic For Dummies, 2nd Edition explains all the varied ways we use logic in philosophy, science, and everyday life. College students taking a logic course and lifelong learners alike can benefit from this accessible guide to logic concepts--such as syllogisms, constructing proofs and refutations, propositional and predicate logic, symbolic logic, modal and fuzzy logic, deductive and inductive reasoning, and beyond. With real-world examples, fun logic problems, and fully worked out proofs, you have plenty of opportunities to follow along and apply what you've learned. Logic For Dummies, 2nd Edition helps you connect the logical dots! Inside: <ul> <li>Grasp formal and informal logic with clear explanations and practice problems</li> <li>See how logic shows up in everyday life and discover how to spot logical fallacies</li> <li>Work through logical proofs and refutations, with answer explanations to strengthen your understanding</li> <li>Sharpen your ability to reason through complex issues with truth tables</li> </ul> Logic For Dummies, 2nd Edition is the logical choice for anyone who wants or needs to learn the concepts, theories, and formulas of logical reasoning.

Logic For Dummies

Collins Kriss Kross

300 Themed Word Grid Puzzles

Collins Bumper Book of Kriss Kross book 1

The Times Puzzle Books

300 Compelling and Addictive Codewords

The Times Bumper Book of Codewords Book 2

Origami Engineering: Combining Science, Art, And Industry

De Gruyter Series in Applied and Numerical Mathematics

Advanced Computational Approaches for Rarefied Flows

Beyond Navier-Stokes

While the field of Deep Learning has been advancing at frightening speeds during the last ten years, we do not have a plausible theoretical explanation of its success yet. One can safely say that Deep Learning works, but nobody really understands why. Nevertheless, starting around five years before now, a decent number of theoretical papers specifically concerning neural nets started to emerge at top machine learning venues. This makes us think that the core of Deep Learning theory has already started to crystallize.The goal of the present book is to present these core concepts of Deep Learning theory to readers so that they could directly dive into recent papers of this area. For this purpose, each chapter elaborates a simple model or a classical result in details first and then discusses possible generalizations and more recent developments of the same idea.We have to warn the reader that the present book is not a mathematical manuscript. Not all results here are stated as formal theorems and not all theorems are provided with complete and rigorous proofs. Many of the theorems of the book are proven up to some technical lemmas, while for some there is only a proof sketch. This conforms with the main idea of the book: present and illustrate concepts rather than reproduce all the results.The book, in its present form, covers the following topics: uniform generalization bounds, PAC-bayesian generalization bounds, double descent phenomena, infinitely-wide networks, implicit bias of gradient descent, loss landscape, gradient descent convergence guarantees, and initialization strategies.

Elements Of Deep Learning Theory

Why Not Nonlinear Algebra: Math, Science, Applications And Funding

This book presents a comprehensive tutorial on propagation, diffraction and scattering problems from the basic principles of physical optics. Beginning with the fundamental differential and integral equations for wavefields, the text presents an exhaustive discussion on the extinction theorem as a non-local boundary condition; this has been extensively employed for the rigorous solution of scattering and diffraction problems. There is also an in-depth presentation of the topic of scattering from rough surfaces, in particular the phenomenon of enhanced backscattering, as well as a detailed development of the angular spectrum representation of fields leading to questions on non-diffraction beams. Of key interest in near field optical microscopy and nanooptics, the S-matrix theory based on the angular spectrum for propagating components and the recently discovered properties of the S-matrix for evanescent components of wavefields are considered. In addition, the book deals with the healing effect of phase conjugation on waves, and focuses on some applications concerning the relationship with time reversal. Readers will also find discussions on image recovery from partial information data (phase problems and super-resolution problems), as well as a chapter on the fundamentals of near field optical microscopy techniques, including the hot topic of propagation in negative index media. This latest edition is expanded to deal with more modern topics in a comprehensive way. Also included is an additional chapter on optomechanics manipulation of nanoparticles.

Scattering And Diffraction In Physical Optics (3rd Edition)

Many-Body Green's Functions for Time-Dependent Problems

This comprehensive compendium addresses a critical need in the AI and machine learning era by bridging foundational information theory (IT) concepts with practical applications in statistical learning. Unlike traditional IT textbooks, this volume emphasizes how IT principles, such as entropy and source coding, underpin modern machine learning techniques like cross-entropy, decision trees, and evidence-lower bounds.This unique book connects IT with probabilistic inference, illustrated through real-world applications, such as decoding LDPC codes for error correction. The inclusion of the Lea probabilistic programming package is particularly valuable for pedagogy, offering students a hands-on tool to solve numerical problems and reinforce theoretical concepts.The useful reference text benefits professionals, researchers, academics and students in the fields of calculus and probability, communications and information sciences.

Introduction To Information Theory And Probabilistic Inference, An

Handbook of MRI Pulse Sequences, Second Edition includes 92 self-contained sections, with each section focusing on a single subject. A new section on detailing the advanced pulse sequence techniques covers a variety of basic and advanced image reconstruction methods. The extensive topic coverage and cross-referencing makes this book ideal for beginners learning the building blocks of MRI pulse sequence design, as well as for experienced professionals who are seeking deeper knowledge of a particular technique. This book is among the most important medical imaging techniques available today. Each of these scanners is capable of running many different "pulse sequences." These sequences are governed by physics and engineering principles and implemented by software programs that control the MRI hardware.

Handbook of MRI Pulse Sequences

Limits and Derivatives of Real Functions for Physicists

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