Neural networks and fuzzy systems

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks.

Text production has many applications.

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars.

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning.

This book is a comprehensive guide that explores the latest developments in anomaly detection techniques across a range of fields, including cybersecurity, finance, image processing, sensor networks, social network analysis, health systems, and IoT systems.

This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) formalism.

This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE).

Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problemsIncludes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook DescriptionThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems.

Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers.

This is a primer written for computer architects in the new and rapidly evolving field of deep learning.

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data.

Discover how to master advanced spaCy techniques, including custom pipelines, LLM integration, and model training, to build NLP solutions efficiently and effectivelyKey FeaturesBuild End-to-End NLP Workflows, From Local Development to Production with Weasel and FastAPIMaster No-Training NLP Development with spaCy-LLM, From Prompt Engineering to Custom TasksCreate Advanced NLP Solutions, From Custom Components to Neural Coreference ResolutionBook DescriptionMastering spaCy, Second Edition is your comprehensive guide to building sophisticated NLP applications using the spaCy ecosystem.

Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problemsIncludes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook DescriptionThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems.

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework.

As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes.

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning.

Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing.

Companies are spending billions on machine learning projects, but its money wasted if the models cant be deployed effectively.

Companies are spending billions on machine learning projects, but its money wasted if the models cant be deployed effectively.

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence.

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence.

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning.

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning.

Learn how to solve challenging machine learning problems with TensorFlow, Googles revolutionary new software library for deep learning.

Learn how to solve challenging machine learning problems with TensorFlow, Googles revolutionary new software library for deep learning.

Recent results and ongoing research in Artificial Intelligence are described in this book, with emphasis on fundamental questions in several key areas: machine learning, neural networks, automated reasoning, natural language processing, and logic methods in AI.

The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously.

This book is the outcome of the International Symposium on Neural Networks for Sensory and Motor Systems (NSMS) held in March 1990 in the FRG.

This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure.

Please note this is a Short Discount publication.

This two-volume proceedings compiles a selection of research papers presented at the ICANN-91.

The goal of neurobionics is to elaborate methods for the repairment and substitution of impaired functions of the human nervous system.

Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production.

This book is an in-depth exploration of brain networks, providing a comprehensive understanding of their structures, functions, and implications for personalization through artificial intelligence.

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis.