Machine learning

This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine.

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey 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 DescriptionMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts.

This two-volume set LNCS 12239-12240 constitutes the refereed proceedings of the 6th International Conference on Artificial Intelligence and Security, ICAIS 2020, which was held in Hohhot, China, in July 2020.

INTERNET OF HEALTHCARE THINGS The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making.

Machine learning and optimization techniques are revolutionizing our world.

Today, emerging technologies offer a new pathway for advancing the economy in the fields of banking, finance, and capital markets.

Is Kubernetes ready for stateful workloads?

Understand and implement big data analysis solutions in pandas with an emphasis on performance.

With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results.

This book opens with an introduction to the main purpose and tasks of the GIANA challenge, as well as a summary and an analysis of the results and performance obtained by the 20 participating teams.

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.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

This contributed volume provides the state-of-the-art development on security and privacy for cyber-physical systems (CPS) and industrial Internet of Things (IIoT).

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020.

Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners.

Deep learning networks are getting smaller.

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

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.

This book describes how neural networks operate from the mathematical point of view.

100 recipes that teach you how to perform various machine learning tasks in the real worldAbout This BookUnderstand which algorithms to use in a given context with the help of this exciting recipe-based guideLearn about perceptrons and see how they are used to build neural networksStuck while making sense of images, text, speech, and real estate?

Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practicesKey FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook DescriptionReinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents.

This book constitutes the refereed post-conference proceedings of the 15th International Conference on Body Area Networks, BodyNets 2020, held in Tallinn, Estonia, in October 2020.

Learn how to use the large-scale natural language processing model developed by OpenAI: ChatGPT.

A solution-based guide to put your deep learning models into production with the power of Apache SparkKey FeaturesDiscover practical recipes for distributed deep learning with Apache SparkLearn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache SparkBook DescriptionWith deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries.

Human visual perception is limited to the visual-optical spectrum.

Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle's Autonomous Database cloud offering.

The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.

Green computing is the emerging practice of using computing and information technology resources more efficiently while maintaining or improving overall performance.

This book brings together the essential methodologies required to understand the advancement of digital technologies into digital transformation, as well as to protect them against cyber threat vulnerabilities (in this context cybersecurity attack ontology is included, modeling different types of adversary knowledge).

Use artificial intelligence (AI) techniques to build tools for auditing your organization.

This book provides readers the "e;big picture"e; and a comprehensive survey of the domain of big data processing systems.

This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models.

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019.

This book offers a comprehensible overview of  Big Data Preprocessing, which includes a formal description of each problem.

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice.

This book reports on research and developments in human-technology interaction.

Get started with artificial intelligence for medical sciences and psychology.

This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models.

Understand how neural networks work and learn how to implement them using TensorFlow 2.

As artificial intelligence (AI) rapidly transforms education, tools like ChatGPT and Claude are revolutionizing the way we teach and learn.

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.

A comprehensive guide to developing neural network-based solutions using TensorFlow 2.

Text production has many applications.

The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process.

In this book, the authors systematically and comprehensively discuss textual emotion classification by using deep broad learning.