Download or read book Machine Learning in Chemistry written by Jon Paul Janet and published by American Chemical Society. This book was released on 2020-05-28 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important

Download or read book Quantum Chemistry in the Age of Machine Learning written by Pavlo O. Dral and published by Elsevier. This book was released on 2022-09-16 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning in Chemistry written by Hugh M. Cartwright and published by Royal Society of Chemistry. This book was released on 2020-07-15 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.

Download or read book Proteomics Data Analysis written by Daniela Cecconi and published by . This book was released on 2021 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.

Download or read book Direct Analysis in Real Time Mass Spectrometry written by Yiyang Dong and published by John Wiley & Sons. This book was released on 2018-04-09 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clear, comprehensive, and state of the art, the groundbreaking book on the emerging technology of direct analysis in real time mass spectrometry Written by a noted expert in the field, Direct Analysis in Real Time Mass Spectrometry offers a review of the background and the most recent developments in DART-MS. Invented in 2005, DART-MS offers a wide range of applications for solving numerous analytical problems in various environments, including food science, forensics, and clinical analysis. The text presents an introduction to the history of the technology and includes information on the theoretical background, for exampleon the ionization mechanism. Chapters on sampling and coupling to different types of mass spectrometers are followed by a comprehensive discussion of a broad range of applications. Unlike most other ionization methods, DART does not require laborious sample preparation, as ionization takes place directly on the sample surface. This makes the technique especially attractive for applications in forensics and food science. Comprehensive in scope, this vital text: -Sets the standard on an important and emerging ionization technique -Thoroughly discusses all the relevant aspects from instrumentation to applications -Helps in solving numerous analytical problems in various applications, for example food science, forensics, environmental and clinical analysis -Covers mechanisms, coupling to mass spectrometers, and includes information on challenges and disadvantages of the technique Academics, analytical chemists, pharmaceutical chemists, clinical chemists, forensic scientists, and others will find this illuminating text a must-have resource for understanding the most recent developments in the field.

Download or read book Ion Mobility Mass Spectrometry written by Giuseppe Paglia and published by Humana. This book was released on 2019-11-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on ion mobility-mass spectrometry (IM-MS) and informatics approaches to improve traditional analysis of molecules by providing fundamentals and protocols for exploiting the potential of state-of-the-art IM-MS technology for the most common analytical applications. The chapters have been organized into four parts, each dealing with a particular set of IM-MS applications: 1) metabolomics and lipidomics; 2) proteomics and glycomics; 3) imaging and ambient ionization IM-MS; and 4) bioinformatic solutions for analyzing IM-MS data and deriving CCS values. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Ion Mobility-Mass Spectrometry: Methods and Protocols serves as an ideal resource for scientists delving into the technique's unprecedented analytical advantages, enabling novel qualitative and quantitative applications for the analysis of complex biological samples.

Download or read book Hands On Mathematics for Deep Learning written by Jay Dawani and published by Packt Publishing Ltd. This book was released on 2020-06-12 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.

Download or read book Ambient Ionization Mass Spectrometry written by Marek Domin and published by Royal Society of Chemistry. This book was released on 2014-11-25 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ambient ionization has emerged as one of the hottest and fastest growing topics in mass spectrometry enabling sample analysis with minimal sample preparation. Introducing the subject and explaining the basic concepts and terminology, this book will provide a comprehensive, unique treatise devoted to the subject. Written by acknowledged experts, there are full descriptions on how new ionization techniques work, with an overview of their strengths, weaknesses and applications. This title will bring the reader right up to date, with both applications and theory, and will be suitable as a tutorial text for those starting in the field from a variety of disciplines.

Download or read book Fundamentals and Applications of Fourier Transform Mass Spectrometry written by Philippe Schmitt-Kopplin and published by Elsevier. This book was released on 2019-08-11 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals and Applications of Fourier Transform Mass Spectrometry is the first book to delve into the underlying principles on the topic and their linkage to industrial applications. Drs. Schmitt-Kopplin and Kanawati have brought together a team of leading experts in their respective fields to present this technique from many different perspectives, describing, at length, the pros and cons of FT-ICR and Orbitrap. Numerous examples help researchers decide which instruments to use for their particular scientific problem and which data analysis methods should be applied to get the most out of their data. - Covers FT-ICR-MS and Orbitrap's fundamentals, enhancing researcher knowledge - Includes details on ion sources, data processing, chemical analysis and imaging - Provides examples across the wide spectrum of applications, including omics, environmental, chemical, pharmaceutical and food analysis

Download or read book Deep Learning with Azure written by Mathew Salvaris and published by Apress. This book was released on 2018-08-24 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.

Download or read book Nanoscale Phenomena in Ferroelectric Thin Films written by Seungbum Hong and published by Springer Science & Business Media. This book was released on 2013-11-27 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the recent advances in the field of nanoscale science and engineering of ferroelectric thin films. It comprises two main parts, i.e. electrical characterization in nanoscale ferroelectric capacitor, and nano domain manipulation and visualization in ferroelectric materials. Well known le'adingexperts both in relevant academia and industry over the world (U.S., Japan, Germany, Switzerland, Korea) were invited to contribute to each chapter. The first part under the title of electrical characterization in nanoscale ferroelectric capacitors starts with Chapter 1, "Testing and characterization of ferroelectric thin film capacitors," written by Dr. I. K. Yoo. The author provides a comprehensive review on basic concepts and terminologies of ferroelectric properties and their testing methods. This chapter also covers reliability issues in FeRAMs that are crucial for commercialization of high density memory products. In Chapter 2, "Size effects in ferroelectric film capacitors: role ofthe film thickness and capacitor size," Dr. I. Stolichnov discusses the size effects both in in-plane and out-of-plane dimensions of the ferroelectric thin film. The author successfully relates the electric performance and domain dynamics with proposed models of charge injection and stress induced phase transition. The author's findings present both a challenging problem and the clue to its solution of reliably predicting the switching properties for ultra-thin ferroelectric capacitors. In Chapter 3, "Ferroelectric thin films for memory applications: nanoscale characterization by scanning force microscopy," Prof. A.

Download or read book Intelligent Systems written by Crina Grosan and published by Springer Science & Business Media. This book was released on 2011-07-29 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.

Download or read book Existing and Potential Standoff Explosives Detection Techniques written by National Research Council and published by National Academies Press. This book was released on 2004-05-14 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Existing and Potential Standoff Explosives Detection Techniques examines the scientific techniques currently used as the basis for explosives detection and determines whether other techniques might provide promising research avenues with possible pathways to new detection protocols. This report describe the characteristics of explosives, bombs, and their components that are or might be used to provide a signature for exploitation in detection technology; considers scientific techniques for exploiting these characteristics to detect explosives and explosive devices; discusses the potential for integrating such techniques into detection systems that would have sufficient sensitivity without an unacceptable false-positive rate; and proposes areas for research that might be expected to yield significant advances in practical explosives and bomb detection technology in the near, mid, and long term.

Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Download or read book Machine Learning and Knowledge Discovery in Databases written by Peggy Cellier and published by Springer Nature. This book was released on 2020-03-27 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019. The chapter "Supervised Human-guided Data Exploration" is published open access under a Creative Commons Attribution 4.0 International license (CC BY).

Download or read book Handbook of Nature Inspired and Innovative Computing written by Albert Y. Zomaya and published by Springer Science & Business Media. This book was released on 2006-01-10 with total page 758 pages. Available in PDF, EPUB and Kindle. Book excerpt: As computing devices proliferate, demand increases for an understanding of emerging computing paradigms and models based on natural phenomena. Neural networks, evolution-based models, quantum computing, and DNA-based computing and simulations are all a necessary part of modern computing analysis and systems development. Vast literature exists on these new paradigms and their implications for a wide array of applications. This comprehensive handbook, the first of its kind to address the connection between nature-inspired and traditional computational paradigms, is a repository of case studies dealing with different problems in computing and solutions to these problems based on nature-inspired paradigms. The "Handbook of Nature-Inspired and Innovative Computing: Integrating Classical Models with Emerging Technologies" is an essential compilation of models, methods, and algorithms for researchers, professionals, and advanced-level students working in all areas of computer science, IT, biocomputing, and network engineering.

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.