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.
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.
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.
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.
This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain.
This book provides a comprehensive analysis of the tools and techniques used today for designing and modeling of efficient and robust swarm-intelligence based systems: highly (or fully) decentralized, semi-autonomous, highly-scalable infrastructures in various real-life scenarios.
This book provides a comprehensive analysis of the tools and techniques used today for designing and modeling of efficient and robust swarm-intelligence based systems: highly (or fully) decentralized, semi-autonomous, highly-scalable infrastructures in various real-life scenarios.
The book emphasizes the role of data in driving healthcare transformation, providing readers with a roadmap for understanding and effectively implementing data-driven innovations.
A compact guide to mastering TensorFlow 2, covering essential APIs, datasets, and practical applications for efficient machine learning and deep learning projects.
A compact guide to mastering TensorFlow 2, covering essential APIs, datasets, and practical applications for efficient machine learning and deep learning projects.
This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare.
The book emphasizes the role of data in driving healthcare transformation, providing readers with a roadmap for understanding and effectively implementing data-driven innovations.
This book introduces an auto design based optimization for building frames using an artificial neural network (ANN) based Lagrange method and novel genetic algorithm (GA).
Turn challenges into opportunities by learning advanced techniques for text generation, summarization, and question answering using LangChain and Google Cloud toolsDRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesSolve real-world business problems with hands-on examples of GenAI applications on Google CloudLearn repeatable design patterns for Gen AI on Google Cloud with a focus on architecture and AI ethicsBuild and implement GenAI agents and workflows, such as RAG and NL2SQL, using LangChain and Vertex AIPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe rapid transformation and enterprise adoption of GenAI has created an urgent demand for developers to quickly build and deploy AI applications that deliver real value.
