Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

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Autor:: Maxime Labonne

Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne - okladka książki

Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne - audiobook MP3

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Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 354
Dostępne formaty:: PDF

ePub

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129,00 zł

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Do przechowalni

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.

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O autorze książki

Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.

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Oceny i opinie klientów: Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

Szczegóły książki

Tytuł oryginału:: Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
ISBN Ebooka:: 978-18-046-1070-1, 9781804610701
Data wydania ebooka :: 2023-04-14 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1
Rozmiar pliku Pdf:: 24.6MB
Rozmiar pliku ePub:: 15.1MB

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Kategorie:
Sztuczna inteligencja
Programowanie » Architektura oprogramowania

Spis treści książki

1. Getting Started with Graph Learning
2. Graph Theory for Graph Neural Networks
3. Creating Node Representations with DeepWalk
4. Improving Embeddings with Biased Random Walks in Node2Vec
5. Including Node Features with Vanilla Neural Networks
6. Introducing Graph Convolutional Networks
7. Graph Attention Networks
8. Scaling Graph Neural Networks with GraphSAGE
9. Defining Expressiveness for Graph Classification
10. Predicting Links with Graph Neural Networks
11. Generating Graphs Using Graph Neural Networks
12. Learning from Heterogeneous Graphs
13. Temporal Graph Neural Networks
14. Explaining Graph Neural Networks
15. Forecasting Traffic Using A3T-GCN
16. Detecting Anomalies Using Heterogeneous Graph Neural Networks
17. Building a Recommender System Using LightGCN
18. Unlocking the Potential of Graph Neural Networks for Real-Word Applications

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