This book introduces the field of deep learning using Python and the powerful Keras library. It offers insights for both novice and experienced machine learning practitioners, and builds your understanding through intuitive explanations and practical examples.

Apr 3, 2025 · Computational graphs are a type of graph that can be used to represent mathematical expressions. This is similar to descriptive language in the case of deep learning models, providing a functional description of the required computation.

Deep Learning Variables are Nodes in GraphSrihari •So far neural networks described with informal graph language •To describe back-propagation it is helpful to use more precise computational graph language •Many possible ways of formalizing computations as graph •Here we use each node as a variable –The variable may be a

The field of graph representation learning has been greatly developed over the past decades that can be roughly divided into three generations includ-ing traditional graph embedding, modern graph embedding, and deep learn-ing on graphs. As …

'The first textbook of Deep Learning on Graphs, with systematic, comprehensive and up-to-date coverage of graph neural networks, autoencoder on graphs, and their applications in natural language processing, computer vision, data mining, biochemistry and healthcare.

Computational graphs. The biggest idea of all of the big ideas about TensorFlow is that the numeric computations are expressed as a computation graph, as shown in the following figure. So, the backbone of any TensorFlow program is going to be a computational graph, where the following is true:

This book covers comprehensive contents in developing deep learning techniques for graph structured data with a specific focus on Graph Neural Networks (GNNs). The foundation of the GNN models are introduced in detail including the two main building operations: graph filtering and pooling operations.

Computational graphs. When performing an operation, for example training a neural network, or the sum of two integers, TensorFlow internally represent, its computation using a data flow graph (or computational graph). This is a directed graph consisting of the following: A set of nodes, each one representing an operation

Sep 23, 2021 · Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications...

Book contents. Frontmatter; Contents; Preface; Acknowledgements; 1 Deep Learning on Graphs: An Introduction; Part I Foundations; Part II Methods; Part III Applications; Part IV Advances; Bibliography; Index