Machine learning

Implement microservices starting with their architecture and moving on to their deployment, manageability, security, and monitoring.

This book provides a thorough overview of the state-of-the-art field-programmable gate array (FPGA)-based robotic computing accelerator designs and summarizes their adopted optimized techniques.

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information.

This book provides a comprehensive explanation of precision (i.

Constraint satisfaction is a simple but powerful tool.

This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform.

Can artificial intelligence learn mathematics?

The computer-aided drug design research field comprises several different knowledge areas, and often, researchers are only familiar or experienced with a small fraction of them.

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book.

This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior.

Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding.

This book provides a systematic and comprehensive overview of machine learning with cognitive science methods and technologies which have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development.

A concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry.

An argument that—despite dramatic advances in the field—artificial intelligence is nowhere near developing systems that are genuinely intelligent.

This two-volume set LNCS 12656 and 12657 constitutes the refereed proceedings of the 43rd European Conference on IR Research, ECIR 2021, held virtually in March/April 2021, due to the COVID-19 pandemic.

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL).

This two-volume set of LNCS 12836 and LNCS 12837 constitutes - in conjunction with the volume LNAI 12838 - the refereed proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021, held in Shenzhen, China in August 2021.

This book describes the diverse applications of Gaussian Process (GP) models in mathematical finance.

Paul Humphreys pioneered philosophical investigations into the methodological revolution begun by computer simulations.

The first book to present the basic techniques and main results of this expanding field of research.

This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science.

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data.

Get to grips with building robust XGBoost models using Python and scikit-learn for deploymentKey FeaturesGet up and running with machine learning and understand how to boost models with XGBoost in no timeBuild real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal resultsDiscover tips and tricks and gain innovative insights from XGBoost Kaggle winnersBook DescriptionXGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.

How developments in science and technology may enable the emergence of purely digital minds—intelligent machines equal to or greater in power than the human brain.

Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights).

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory.

This book presents a curated selection of papers from the International Conference on Advanced Engineering, Technology, and Applications (ICAETA24), hosted by the University of Catania, Italy, in March 2024.

This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.

Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This BookResolve complex machine learning problems and explore deep learningLearn to use Python code for implementing a range of machine learning algorithms and techniquesA practical tutorial that tackles real-world computing problems through a rigorous and effective approachWho This Book Is ForThis title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science.

Covers text mining and link detection algorithms, pre-processing techniques, knowledge representation, visualization and applications.

This book constitutes the proceedings of the 7th International Conference on Decision Support Systems Technologies, ICDSST 2021, held during May 26-28, 2021.

Modeling Visual Aesthetics, Emotion, and Artistic Style offers a comprehensive exploration of the increasingly significant topic of the complex interplay between human perception and digital technology.

This book constitutes revised selected papers from the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held during October 15-16, 2020.

This updated second edition offers a guided tour of machine learning algorithms and architecture design.

This book constitutes the proceedings of the international workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland, in September 2021.

This book is the proceeding of the 1st International Conference on Distributed Sensing and Intelligent Systems (ICDSIS2020) which will be held in The National School of Applied Sciences of Agadir, Ibn Zohr University, Agadir, Morocco on February 01-03, 2020.

This book contains the topics of artificial intelligence and deep learning that do have much application in real-life problems.

This book is a collection of selected research papers presented at the 2023 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), held in Berlin, Germany, on June 30 - July 2, 2023.

AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONSPROSE Award Finalist 2019 Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning.

Your hands-on reference guide to developing, training, and optimizing your machine learning modelsKey FeaturesYour guide to learning efficient machine learning processes from scratchExplore expert techniques and hacks for a variety of machine learning conceptsWrite effective code in R, Python, Scala, and Spark to solve all your machine learning problemsBook DescriptionMachine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques.

This book is an invaluable reference for those operating within the fields of Cyber Security, Digital Forensics, Digital Policing, Computer Science and Artificial Intelligence.

This book constitutes the proceedings of the 6th International Conference on Business Intelligence, CBI 2021, which took place in Beni Mellal, Morocco, during May 27-29, 2021.

This book discusses a multidisciplinary and multi-stakeholder collaborative approach to small population clinical trials.