Neural networks and fuzzy systems

Master the art of building AI agents with this hands-on guide to orchestration, multi-agent systems, real-world case studies, and ethical insights to drive immediate business impactKey FeaturesBuild production-ready AI agents with hands-on tutorials for diverse industry applicationsExplore multi-agent system architectures with practical frameworks for orchestrator comparisonFuture-proof your AI development with ethical implementation strategies and security patternsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs AI agents evolve to take on complex tasks and operate autonomously, you need to learn how to build these next-generation systems.

Artificial Intelligence from Science Fiction to Reality examines various aspects, starting with the evolution of human and artificial intelligence (AI).

Deep learning systems have gotten really great at identifying patterns in text, images, and video.

AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory.

How does the computer learn to understand what it sees?

Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks.

Discover valuable machine learning techniques you can understand and apply using just high-school math.

Summary Machine learning (ML) is a collection of programming techniques for discovering relationships in data.

Summary You are going to need more than technical knowledge to succeed as a data scientist.

SummaryGrokking Deep Learning teaches you to build deep learning neural networks from scratch!

Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems.

Sharpen your coding skills by exploring established computer science problems!

Summary Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists.

SummaryDeep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques.

From start to finish, the best book to help you learn AI algorithms and recall why and how you use them.

There's a great deal of wisdom in a crowd, but how do you listen to a thousand people talking at once?

Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters!

SummaryDeep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface.

Summary Deep learning has transformed the fields of computer vision, image processing, and natural language applications.

Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and back-office tasks Envision quickly gauging customer sentiment from social media content (even large volumes of it).

SummaryDeep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI.

Deep learning from the ground up using R and the powerful Keras library!

Artificial Intelligence from Science Fiction to Reality examines various aspects, starting with the evolution of human and artificial intelligence (AI).

Master the art of building AI agents with this hands-on guide to orchestration, multi-agent systems, real-world case studies, and ethical insights to drive immediate business impactKey FeaturesBuild production-ready AI agents with hands-on tutorials for diverse industry applicationsExplore multi-agent system architectures with practical frameworks for orchestrator comparisonFuture-proof your AI development with ethical implementation strategies and security patternsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs AI agents evolve to take on complex tasks and operate autonomously, you need to learn how to build these next-generation systems.

In today's fast-evolving tech landscape, Intelligent Networks and Systems: Advanced Technologies and Applications explores cutting-edge innovations in intelligent systems and their real-world impact.

This volume covers a comprehensive range of fundamental concepts in deep learning and artificial neural networks, making it suitable for beginners looking to learn the basics.

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management.

This volume covers a comprehensive range of fundamental concepts in deep learning and artificial neural networks, making it suitable for beginners looking to learn the basics.

Image processing plays a crucial role in various fields, including digital multimedia, automated vision detection and inspection, and pattern recognition.

In today's fast-evolving tech landscape, Intelligent Networks and Systems: Advanced Technologies and Applications explores cutting-edge innovations in intelligent systems and their real-world impact.

Can machines think?

Can machines think?

Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric and DGLFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph Machine Learning, Second Edition builds on its predecessor s success, delivering the latest tools and techniques for this rapidly evolving field.

Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric and DGLFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph Machine Learning, Second Edition builds on its predecessor s success, delivering the latest tools and techniques for this rapidly evolving field.

This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies.

Chat-GPT, humanoid robotics, and self-driving cars are just a few of the things that are changing our everyday lives.

Chat-GPT, humanoid robotics, and self-driving cars are just a few of the things that are changing our everyday lives.

This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for real-world implementation.

Diagnosis through images, robot surgeons, digital twins, and the metaverse are some of the applications in which artificial intelligence (AI) is involved.

Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndexPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionBecome an expert in Generative AI through immersive, hands-on projects that leverage today's most powerful models for Natural Language Processing (NLP) and computer vision.

After many years during which it languished in relative obscurity, in remote classrooms of computer science departments and in small prototype projects for tech companies, artificial intelligence (AI) is now a searingly hot topic across the media.

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management.

After many years during which it languished in relative obscurity, in remote classrooms of computer science departments and in small prototype projects for tech companies, artificial intelligence (AI) is now a searingly hot topic across the media.

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.

AI for Knowledge explores the question of whether artificial intelligence (AI) is transforming knowledge access and creation for the good.

Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source librariesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore beautiful, customized charts and infographics in full colorWork with fully functional OO code using open source libraries in the Python Notebook for each chapterUnleash the potential of real-world datasets with practical data augmentation techniquesBook DescriptionData is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust.

Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source librariesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore beautiful, customized charts and infographics in full colorWork with fully functional OO code using open source libraries in the Python Notebook for each chapterUnleash the potential of real-world datasets with practical data augmentation techniquesBook DescriptionData is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust.

As multicriteria decision-making (MCDM) continues to grow and evolve, machine learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems.