TinyML
TinyML

Author: Pete Warden

Publisher: O'Reilly Media

Published: 2019-12-16

Total Pages: 504

ISBN-13: 1492052019

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Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Quantum Chemistry in the Age of Machine Learning
Quantum Chemistry in the Age of Machine Learning

Author: Pavlo O. Dral

Publisher: Elsevier

Published: 2022-09-16

Total Pages: 702

ISBN-13: 0323886043

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Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field. - Compiles advances of machine learning in quantum chemistry across different areas into a single resource - Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry - Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry

The Principles of Deep Learning Theory
The Principles of Deep Learning Theory

Author: Daniel A. Roberts

Publisher: Cambridge University Press

Published: 2022-05-26

Total Pages: 473

ISBN-13: 1316519333

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This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

The Minimum Description Length Principle
The Minimum Description Length Principle

Author: Peter D. Grünwald

Publisher: MIT Press

Published: 2007

Total Pages: 736

ISBN-13: 0262072815

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This introduction to the MDL Principle provides a reference accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection.

Artificial Intelligence For Science: A Deep Learning Revolution
Artificial Intelligence For Science: A Deep Learning Revolution

Author: Alok Choudhary

Publisher: World Scientific

Published: 2023-03-21

Total Pages: 803

ISBN-13: 9811265682

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This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.

Artificial Intelligence and Industry in Society 5.0
Artificial Intelligence and Industry in Society 5.0

Author: Nitin Liladhar Rane

Publisher: Deep Science Publishing

Published: 2024-10-13

Total Pages: 123

ISBN-13: 8198127119

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The past few years have seen artificial intelligence (AI) acting as a force that has been changing industries, societies and also the educational landscape. The objective of this book is to present a holistic view of the different sectors being affected by AI and to list some of the challenges or opportunities that have arisen as part of this fast-moving area. The opening chapter is on the ethically fraught domain of AI technologies such as ChatGPT in educational contexts, noting new frontiers for cheating and suggesting ways that its integrity can be protected during this next industrial push of technological change. Even as AI tools grow in common use, educational institutions must grapple with these complexities to maintain notions of fair play and knowledge building. Further chapters move beyond AI in education to how it can be used as a broad lever for smart and sustainable campuses, cities, and infrastructure. The text in Chapter two centers on the way artificial intelligence (machine learning and deep learning) can steer more insightful urban planning, resource management and development that is sustainable. Chapter three presents a wider coverage of AI applications, including the concept of digital twins in different sectors-healthcare, finance and agriculture-as examples on how digital replicas improve productivity and innovation across various industries under Industry 4.0; 5.0 and Society 5.0. Chater four and five moves to the regulatory issues regarding AI. They talk about the importance of strong policies and the technological, economic, and regulatory obstacles holding back AI from realizing its promise in helping industries become smarter and more sustainable. The book also wraps up with a reflective commentary which presents the real-world applications of AI, future directions and potential research topics in AI, thereby providing readers some suggestions about where we could go regarding the development of AI in the next few years. This is the series of chapters that will show you how transformational AI can be; we hope it awakens the imagination and motivates people to conduct research and innovation in this exciting sector.

High Performance Computing
High Performance Computing

Author: Philippe Navaux

Publisher: Springer Nature

Published: 2022-12-20

Total Pages: 246

ISBN-13: 3031238214

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This book constitutes the proceedings of the 9th Latin American Conference on High Performance Computing, CARLA 2022, held in Porto Alegre, Brazil, in September 2022. The 16 full papers presented in this volume were carefully reviewed and selected from 56 submissions. CARLA, the Latin American High Performance Computing Conference, is an international academic meeting aimed at providing a forum to foster the growth and strength of the High Performance Computing (HPC) community in Latin America and the Caribbean through the exchange and dissemination of new ideas, techniques, and research in HPC and its application areas.

Formation and Evolution of Galaxy Outskirts (IAU S321)
Formation and Evolution of Galaxy Outskirts (IAU S321)

Author: Armando Gil de Paz

Publisher: Cambridge University Press

Published: 2017-02-28

Total Pages:

ISBN-13: 9781107169883

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The borders between galaxies and the almost empty intergalactic medium are ill-defined regions where gas struggles to form stars. The proceedings of IAU Symposium 321 summarize our current understanding of the rarefied universe and prepare for the optimal exploitation of upcoming astronomical instruments. They discuss the most recent advances in the study of the stellar, dust and gas content of galaxy outskirts, going from resolved stellar populations in the Milky Way and in the Local Group to the study of high-redshift systems. Such a broad approach, both in terms of galaxy components and evolutionary epochs, is necessary to take full advantage of the recent discoveries made by facilities at all wavelengths, to deepen our knowledge of the assembly and evolution of these elusive regions and to establish their role within the evolution of galaxies as a whole and their interactions with the surrounding intergalactic medium.

Machine Learning Meets Quantum Physics
Machine Learning Meets Quantum Physics

Author: Kristof T. Schütt

Publisher: Springer Nature

Published: 2020-06-03

Total Pages: 473

ISBN-13: 3030402452

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Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

A Primer on Machine Learning in Subsurface Geosciences
A Primer on Machine Learning in Subsurface Geosciences

Author: Shuvajit Bhattacharya

Publisher: Springer Nature

Published: 2021-05-03

Total Pages: 172

ISBN-13: 3030717682

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This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.