Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBook

Key Features

Implement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applications

Book Description

Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.

What you will learn

Understand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problems

Who this book is for

This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What you will learnUnderstand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is forThis book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Kick-start your agentic AI career with insights from leaders at Google, Microsoft, and Liquid AI. Learn the skills, strategies, and tools to build intelligent agents, transition faster, and lead in the era of autonomous systems.Key FeaturesDiscover the only comprehensive career roadmap for the Generative AI eraLearn directly from top industry experts, leaders, and AI innovatorsBuild future-proof skills, from prompt engineering to ethical AI strategyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis book is a landmark publication, the first-ever collaborative guide authored by a powerhouse lineup of industry leaders from Google, Microsoft, IBM and Liquid AI. This all-in-one resource demystifies the Agentic AI revolution and delivers a practical, career-centered framework for every professional, from graduates to senior executives.You'll start by understanding Gen AI's impact on the job market and decoding core concepts like LLMs, transformers, and prompt engineering. With detailed role descriptions and leveling matrices to identify and build a roadmap tailored to your background.Next, dive into the Gen AI tech stack: RAG architectures, vector databases, function calling, multi-agent systems, and LLMOps. Explore essential frameworks including LangChain, LlamaIndex, Haystack, & Hugging Face, learning when and how to apply each tool effectively. Beyond technical skills, you'll cultivate the human advantages AI cannot replicate, critical thinking, adaptability, creativity, and ethical judgment. Real-world use cases span finance, healthcare, software development, and marketing, demonstrating how Gen AI transforms industries.More than a technical guide, this is a career transformation blueprint. It integrates practical frameworks for skill development, job search strategies, and personal branding in an AI-augmented world.What you will learnUnderstand the global impact of Generative and Agentic AI on jobs and industriesMaster key GenAI & Agentic AI concepts for any professional backgroundIdentify your best-fit AI career path and transferable skillsBuild familiarity with LlamaIndex, Haystack, Hugging Face, and core RAG workflowsBuild an AI-enhanced personal brand using the 5-Layer LinkedIn Strategy and Portfolio Showcase FrameworkApply ethical and responsible AI principles and governance in real-world practiceCreate a lifelong learning and upskilling roadmap for sustained growthWho this book is forThis book is for graduates, professionals, and leaders seeking to future-proof their careers and capitalize on the rise of Generative AI. Whether you’re entering the job market, transitioning from a traditional tech or non-tech role, or aiming to lead AI transformation, this guide provides the clarity, tools, and confidence to take control of your trajectory. It’s an indispensable resource for engineers, data scientists, product managers, business analysts, educators, and executives determined to stay relevant in the new world of work.

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBook

Key Features

Implement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applications

Book Description

Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.

What you will learn

Understand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problems

Who this book is for

This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What you will learnUnderstand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is forThis book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Kick-start your agentic AI career with insights from leaders at Google, Microsoft, and Liquid AI. Learn the skills, strategies, and tools to build intelligent agents, transition faster, and lead in the era of autonomous systems.Key FeaturesDiscover the only comprehensive career roadmap for the Generative AI eraLearn directly from top industry experts, leaders, and AI innovatorsBuild future-proof skills, from prompt engineering to ethical AI strategyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis book is a landmark publication, the first-ever collaborative guide authored by a powerhouse lineup of industry leaders from Google, Microsoft, IBM and Liquid AI. This all-in-one resource demystifies the Agentic AI revolution and delivers a practical, career-centered framework for every professional, from graduates to senior executives.You'll start by understanding Gen AI's impact on the job market and decoding core concepts like LLMs, transformers, and prompt engineering. With detailed role descriptions and leveling matrices to identify and build a roadmap tailored to your background.Next, dive into the Gen AI tech stack: RAG architectures, vector databases, function calling, multi-agent systems, and LLMOps. Explore essential frameworks including LangChain, LlamaIndex, Haystack, & Hugging Face, learning when and how to apply each tool effectively. Beyond technical skills, you'll cultivate the human advantages AI cannot replicate, critical thinking, adaptability, creativity, and ethical judgment. Real-world use cases span finance, healthcare, software development, and marketing, demonstrating how Gen AI transforms industries.More than a technical guide, this is a career transformation blueprint. It integrates practical frameworks for skill development, job search strategies, and personal branding in an AI-augmented world.What you will learnUnderstand the global impact of Generative and Agentic AI on jobs and industriesMaster key GenAI & Agentic AI concepts for any professional backgroundIdentify your best-fit AI career path and transferable skillsBuild familiarity with LlamaIndex, Haystack, Hugging Face, and core RAG workflowsBuild an AI-enhanced personal brand using the 5-Layer LinkedIn Strategy and Portfolio Showcase FrameworkApply ethical and responsible AI principles and governance in real-world practiceCreate a lifelong learning and upskilling roadmap for sustained growthWho this book is forThis book is for graduates, professionals, and leaders seeking to future-proof their careers and capitalize on the rise of Generative AI. Whether you’re entering the job market, transitioning from a traditional tech or non-tech role, or aiming to lead AI transformation, this guide provides the clarity, tools, and confidence to take control of your trajectory. It’s an indispensable resource for engineers, data scientists, product managers, business analysts, educators, and executives determined to stay relevant in the new world of work.