Machine learning

This book provides an introduction to next generation smart screening technology for medical image analysis that combines artificial intelligence (AI) techniques with digital screening to develop innovative methods for detecting breast cancer.

Progress in the application of machine learning (ML) to the physical and life sciences has been rapid.

Learn how to apply test-driven development (TDD) to machine-learning algorithmsand catch mistakes that could sink your analysis.

A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data.

The 4-volume set LNCS 13019, 13020, 13021 and 13022 constitutes the refereed proceedings of the 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021, held in Beijing, China, in October-November 2021.

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks.

This volume constitutes the proceedings of the 12th International Conference on Social Informatics, SocInfo 2020, held in Pisa, Italy, in October 2020.

If you want to work in any computational or technical field, you need to understand linear algebra.

The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019.

Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging.

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry.

This book addresses emerging issues concerning the integration of artificial intelligence systems in our daily lives.

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

A self-contained and practical introduction that assumes no prior knowledge of programming or machine learning.

This book constitutes the refereed proceedings of the 27th China Conference on Information Retrieval, CCIR 2021, held in Dalian, China, in October 2021.

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.

This book features best selected research papers presented at the Fourth International Conference on Machine Learning, Internet of Things and Big Data (ICMIB 2024) held at GIET University, Gunupur, India, during 8-10 April 2024.

Over the next few decades, machine learning and data science will transform the finance industry.

This book contains original, peer-reviewed research articles from the 5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, held in Hyderabad, India on 28-29 March 2024.

Build smart applications by implementing real-world artificial intelligence projectsKey FeaturesExplore a variety of AI projects with PythonGet well-versed with different types of neural networks and popular deep learning algorithmsLeverage popular Python deep learning libraries for your AI projectsBook DescriptionArtificial Intelligence (AI) is the newest technology that's being employed among varied businesses, industries, and sectors.

This book focuses on Entity Discovery and Linking (EDL), which is the problem of identifying concepts and entities, disambiguating them, and grounding them to one or more knowledge bases (KBs).

This book continues the previous edition: Samsul Ariffin Abdul Karim (2022).

Introduces fundamental theory, basic and advanced algorithms, and system design issues.

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques.

The two-volume set LNCS 12615 + 12616 constitutes the refereed proceedings of the 12th International Conference on Intelligent Human Computer Interaction, IHCI 2020, which took place in Daegu, South Korea, during November 24-26, 2020.

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

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020.

Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasksKey FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning.

This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020.

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity.

This book introduces and provides an analysis of the basic concepts of algorithms, data, and computation and discusses the role of algorithms in ruling and shaping our world.

This book constitutes the refereed proceedings of the 14th International Conference on Scalable Uncertainty Management, SUM 2020, which was held in Bozen-Bolzano, Italy, in September 2020.

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology.

Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results.

This important book introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS.

Discover the capabilities of PySpark and its application in the realm of data science.

This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics.

This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases.

This book covers both classical and modern models in deep learning.

Membrane computing is a class of distributed and parallel computing models inspired by living cells.

Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note.

This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management.

An examination of the implications for society of rapidly advancing artificial intelligence systems, combining a humanities perspective with technical analysis; includes exercises and discussion questions.

This book will help you comprehend the impact of artificial intelligence (AI) on information security, data privacy, and data security.

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

The design patterns in this book capture best practices and solutions to recurring problems in machine learning.

Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.

Named one of The five best books to understand AI by The EconomistThe impact AI will have is profound, but the economic framework for understanding it is surprisingly simple.