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Paperback or Softback. Zustand: New. Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch. Book.

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Paperback. Zustand: New. LLM Engineering offers a detailed roadmap for building, training, and deploying Large Language Models, complete with practical examples and advanced techniques, making it an essential guide for modern AI professionals.

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Paperback. Zustand: New. LLM Engineering offers a detailed roadmap for building, training, and deploying Large Language Models, complete with practical examples and advanced techniques, making it an essential guide for modern AI professionals.

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Paperback. Zustand: New. LLM Engineering offers a detailed roadmap for building, training, and deploying Large Language Models, complete with practical examples and advanced techniques, making it an essential guide for modern AI professionals.

Paperback. Zustand: New. LLM Engineering offers a detailed roadmap for building, training, and deploying Large Language Models, complete with practical examples and advanced techniques, making it an essential guide for modern AI professionals.

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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Elektronisches BuchKey Features:Implement state-of-the-art graph neural network architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description:Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social 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 problemsWho 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.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This definitive playbook is written by a team of industry leaders and domain experts, it equips professionals, graduates with the mindset, skills, and strategies needed to succeed, pivot, and lead in a world transformed by intelligent systems.…

Taschenbuch. Zustand: Neu. Hands-On Graph Neural Networks Using Python | Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch | Maxime Labonne | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781804617526 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practicesPurchase of the print or Kindle book includes a free Elektronisches Buch in PDF format'This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.' - Julien Chaumond, CTO and Co-founder, Hugging FaceBook DescriptionThis LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. The guide walks you through building an LLM-powered twin that's cost-effective, scalable, and modular. It moves beyond isolated Jupyter Not Elektronisches Buch, focusing on how to build production-grade end-to-end LLM systems.Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.What you will learn- Implement robust data pipelines and manage LLM training cycles- Create your own LLM and refine with the help of hands-on examples- Get started with LLMOps by diving into core MLOps principles like IaC- Perform supervised fine-tuning and LLM evaluation- Deploy end-to-end LLM solutions using AWS and other tools- Explore continuous training, monitoring, and logic automation- Learn about RAG ingestion as well as inference and feature pipelinesWho this book is forThis book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.Table of Contents- Undersstanding the LLM Twin Concept and Architecture- Tooling and Installation- Data Engineering- RAG Feature Pipeline- Supervised Fine-tuning- Fine-tuning with Preference Alignment- Evaluating LLMs- Inference Optimization- RAG Inference Pipeline- Inference Pipeline Deployment- MLOps and LLMOps- Appendix: MLOps Principles.…

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