Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories.
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.
Master advanced spaCy techniques, including custom pipelines, LLM integration, and model training, to build NLP solutions efficientlyKey FeaturesBuild end-to-end NLP workflows, from local development to production, using Weasel and FastAPIMaster no-training NLP development with spacy-llm, covering everything from prompt engineering to custom tasksCreate advanced NLP solutions, including custom components and neural coreference resolutionPurchase of the print or Kindle book includes a free PDF eBookBook 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.
Healthcare operations, in hospitals and home healthcare settings, are inundated with complex fuzzy features that impose difficulties in the creation of work schedules.
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.
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.
This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure.
Fuzzy social choice theory is useful for modeling the uncertainty and imprecision prevalent in social life yet it has been scarcely applied and studied in the social sciences.
Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments.
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.
Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on mannerKey Features[*] Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide[*] Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more[*] Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examplesBook DescriptionDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions.
As the increased demand for high-speed communication creates an interest in the development of optical networks, intelligent all optical networks have emerged as the next generation for reliable and fast connections.
With artificial neural network research being one of the new directions for new generation computers, current research suggests that open-box artificial higher order neural networks (HONNs) play an important role in this new direction.
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them.
Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks.
Based on the authors' groundbreaking research, Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology presents a research ideology, a novel multi-paradigm methodology, and advanced computational models for the automated EEG-based diagnosis of neurological disorders.
In contrast to classical image analysis methods that employ "e;crisp"e; mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks.
The Industrial Electronics Handbook, Second Edition combines traditional and newer, more specialized knowledge that will help industrial electronics engineers develop practical solutions for the design and implementation of high-power applications.
