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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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Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Artículo nº.: 83394025

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

86% de los encuestados recomendarían esto a un amigo

Artículo nº.: 83394025

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Shop Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch online at a best price in España. 1804617520
  • 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.Table of ContentsGetting Started with Graph LearningGraph Theory for Graph Neural NetworksCreating Node Representations with DeepWalkImproving Embeddings with Biased Random Walks in Node2VecIncluding Node Features with Vanilla Neural NetworksIntroducing Graph Convolutional NetworksGraph Attention NetworksScaling Graph Neural Networks with GraphSAGEDefining Expressiveness for Graph ClassificationPredicting Links with Graph Neural NetworksGenerating Graphs Using Graph Neural NetworksLearning from Heterogeneous GraphsTemporal Graph Neural NetworksExplaining Graph Neural NetworksForecasting Traffic Using A3T-GCNDetecting Anomalies Using Heterogeneous Graph Neural NetworksBuilding a Recommender System Using LightGCNUnlocking the Potential of Graph Neural Networks for Real-Word Applications
Publisher Packt Publishing
Publication date April 14, 2023
Edition 1st
Language English
Print length 354 pages
ISBN-10 1804617520
ISBN-13 978-1804617526
Item Weight 1.43 pounds (650 grams)
Dimensions 7.5 x 0.8 x 9.25 inches (19.1 x 2 x 23.5 cm)

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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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Intelligence & Semantics Editorial Review

The book "Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch" provides a good overview of the approaches and techniques used in graph neural networks. It also serves as a helpful introduction to the packages available for GNNs in Python. However, some weaknesses were identified in the reviews, such as the lack of detailed implementation explanations, glossing over key concepts, and not delving into why GNNs work better than other techniques in-depth. The book is noted for its clear explanations and functioning examples, making it suitable for individuals with basic ML/DL knowledge looking to delve into GNNs. While the physical book is of good quality, some readers found it cumbersome to use, suggesting opting for the free PDF version for a better reading experience. **

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Ventajas

  • Great overview of GNN approaches and techniques
  • Good introduction to Python packages for GNNs
  • Clear explanations and functioning examples

Desventajas

  • Lack of detailed implementation explanations

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