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This book introduces "Random Tensors," a framework for studying random geometries in any dimension. It provides a complete derivation of the key results in the field. Whatever form a theory of Quantum Gravity may take, it must incorporate random geometry.
Become a machine learning pro! Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you’ll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning—all without ever losing your cool! Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence. Install TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with TensorBoard Perform image recognition with convolutional neural networks (CNNs) Analyze sequential data with recurrent neural networks (RNNs) Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP) If you’re a manager or software developer looking to use TensorFlow for machine learning, this is the book you’ll want to have close by.
Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks KEY FEATURES ● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts. ● Includes practical demonstration of robust deep learning prediction models with exciting use-cases. ● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence. DESCRIPTION This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch. The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task. Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques. WHAT YOU WILL LEARN ● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics. ● Learn the basics of neural architecture search with Neural Network Intelligence. ● Combine standard statistical analysis methods with deep learning approaches. ● Automate the search for optimal predictive architecture. ● Design your custom neural network architecture for specific tasks. ● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes. WHO THIS BOOK IS FOR This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed. TABLE OF CONTENTS 1. Time Series Problems and Challenges 2. Deep Learning with PyTorch 3. Time Series as Deep Learning Problem 4. Recurrent Neural Networks 5. Advanced Forecasting Models 6. PyTorch Model Tuning with Neural Network Intelligence 7. Applying Deep Learning to Real-world Forecasting Problems 8. PyTorch Forecasting Package 9. What is Next?
C N Yang is a towering figure of science who has significantly extended human understanding of nature, headed one of the foremost research institutes in physics for three decades, and made great contributions to the advances of Chinese physics. This Festschrift in honor of Professor Yang on his centenary birthday consists of two volumes: Volume A consists of general essays concerning Professor Yang the person, as well as the authors' impressions and reminiscences of him, which are mainly (but not exclusively) in Chinese. This volume, that is Volume B, consists of over thirty scientific papers in English on subjects broadly related to his work and contributed by two different groups: Professor Yang's colleagues, friends, and former students; and graduates from the Tsinghua University Physics Department or Institute for Advanced Study, who have come under the influence of Professor Yang and are now established in their own careers; review papers presented at a Symposium held in his honor in 2021 are also included. It is hoped that this Festschrift can serve as a fit tribute to Professor Yang's lifelong achievements, and also increase public awareness of the many different sides of this giant — his life, his personality, his work, his influence, as well as what he strives for.
Special numerical techniques are already needed to deal with nxn matrices for large n.Tensor data are of size nxnx...xn=n^d, where n^d exceeds the computer memory by far. They appear for problems of high spatial dimensions. Since standard methods fail, a particular tensor calculus is needed to treat such problems. The monograph describes the methods how tensors can be practically treated and how numerical operations can be performed. Applications are problems from quantum chemistry, approximation of multivariate functions, solution of pde, e.g., with stochastic coefficients, etc. ​
Following the fundamental insights from quantum mechanics and general relativity, geometry itself should have a quantum description; the search for a complete understanding of this description is what drives the field of quantum gravity. Group field theory is an ambitious framework in which theories of quantum geometry are formulated, incorporating successful ideas from the fields of matrix models, ten-sor models, spin foam models and loop quantum gravity, as well as from the broader areas of quantum field theory and mathematical physics. This special issue collects recent work in group field theory and these related approaches, as well as other neighbouring fields (e.g., cosmology, quantum information and quantum foundations, statistical physics) to the extent that these are directly relevant to quantum gravity research.
This book brings together contributions by leading researchers in computational complexity theory written in honor of Somenath Biswas on the occasion of his sixtieth birthday. They discuss current trends and exciting developments in this flourishing area of research and offer fresh perspectives on various aspects of complexity theory. The topics covered include arithmetic circuit complexity, lower bounds and polynomial identity testing, the isomorphism conjecture, space-bounded computation, graph isomorphism, resolution and proof complexity, entropy and randomness. Several chapters have a tutorial flavor. The aim is to make recent research in these topics accessible to graduate students and senior undergraduates in computer science and mathematics. It can also be useful as a resource for teaching advanced level courses in computational complexity.
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.
This book provides a number of combinatorial tools that allow a systematic study of very general discrete spaces involved in the context of discrete quantum gravity. In any dimension D, we can discretize Euclidean gravity in the absence of matter over random discrete spaces obtained by gluing families of polytopes together in all possible ways. These spaces are then classified according to their curvature. In D=2, it results in a theory of random discrete spheres, which converge in the continuum limit towards the Brownian sphere, a random fractal space interpreted as a quantum random space-time. In this limit, the continuous Liouville theory of D=2 quantum gravity is recovered. Previous results in higher dimension regarded triangulations, converging towards a continuum random tree, or gluings of simple building blocks of small sizes, for which multi-trace matrix model results are recovered in any even dimension. In this book, the author develops a bijection with stacked two-dimensional discrete surfaces for the most general colored building blocks, and details how it can be used to classify colored discrete spaces according to their curvature. The way in which this combinatorial problem arrises in discrete quantum gravity and random tensor models is discussed in detail.
This volume focuses on developments in the field of group theory in its broadest sense and is of interest to theoretical and experimental physicists, mathematicians, and scientists in related disciplines who are interested in the latest methods and applications. In an increasingly ultra-specialized world, this volume will demonstrate the interchange of ideas and methods in theoretical and mathematical physics.