Pro Deep Learning with TensorFlow A Mathematical Approach to Advanced Artificial Intelligence in Python

Mathematics

Kanemitsu Shigeru, Tsukada Haruo, “Contributions To The Theory Of Zeta-Functions: The Modular Relation Supremacy”
The Original Code in the Bible: Using Science and Mathematics to Reveal God’s Fingerprints by Del Washburn
Hellmut Baumgartel, Manfred Wollenberg, “Causal Nets of Operator Algebras: Mathematical Aspects of Algebraic Quantum Field Theory”
Hellmut Baumgartel, “Operator Algebraic Methods in Quantum Field Theory: A Series of Lectures”
Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python By Santanu Pattanayak

Kanemitsu Shigeru, Tsukada Haruo, “Contributions To The Theory Of Zeta-Functions: The Modular Relation Supremacy”

2015 | ISBN-10: 981444961X | 316 pages | PDF | 7 MB

This volume provides a systematic survey of almost all the equivalent assertions to the functional equations – zeta symmetry – which zeta-functions satisfy, thus streamlining previously published results on zeta-functions. The equivalent relations are given in the form of modular relations in Fox H-function series, which at present include all that have been considered as candidates for ingredients of a series. The results are presented in a clear and simple manner for readers to readily apply without much knowledge of zeta-functions.
This volume aims to keep a record of the 150-year-old heritage starting from Riemann on zeta-functions, which are ubiquitous in all mathematical sciences, wherever there is a notion of the norm. It provides almost all possible equivalent relations to the zeta-functions without requiring a reader’s deep knowledge on their definitions. This can be an ideal reference book for those studying zeta-functions.

The Original Code in the Bible: Using Science and Mathematics to Reveal God’s Fingerprints by Del Washburn

English | October 1st, 1998 | ISBN: 1568331150 | 252 Pages | PDF | 10.07 MB

This ground-breaking work explains the Bible’s fundamental mathematical code in easy-to-understand terms.

Hellmut Baumgartel, Manfred Wollenberg, “Causal Nets of Operator Algebras: Mathematical Aspects of Algebraic Quantum Field Theory”

1995 | ISBN-10: 3055016556 | 228 pages | PDF | 14 MB

In the course of lectures, held from summer 1993 up to summer 1994 at the Humbolt University of Berlin (SS 93), the Technical University of Berlin (WS 93/94) and the University of Potsdam (SS 94) the author presents basic operatoralgebraic material which is necessary to establish basic concepts of the algebraic quantum field theory as well as to get essential results in this field. The original ansatz of R. Haag (and others) started with the “working hypothesis” of a net of algebras of local observables. The aim of the lectures is to show that the success of this ansatz is strongly connected with deep results in the theory of operator algebras. The emphasis is to make the material presented clear and readable without missing depth. The hope is to convince the reader of the beauty and stringency of this theory.

Hellmut Baumgartel, “Operator Algebraic Methods in Quantum Field Theory: A Series of Lectures”

2017 | ISBN-10: 3055014219 | 473 pages | PDF | 31 MB

For advanced students in mathematics and mathematicians, as well as theoretical physicists, this volume presents the theory of nets of operator algebras, in particular nets connected with a causality condition. Such nets appear in mathematical formulations of quantum statistical mechanics and of quantum field theory. In this volume, the emphasis lies on nets which are linked with the algebraic approach to quantum field theory. Assumes a basic knowledge of functional analysis, in particular in the field of operator algebras. Annotation copyright Book News, Inc. Portland, Or.

Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python By Santanu Pattanayak

English | PDF,EPUB | 2017 | 412 Pages | ISBN : 1484230957 | 22.77 MB

Deploy deep learning solutions in production with ease using TensorFlow. You’ll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.
Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures.
All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways.
You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.