Course: Graph Neural Networks (GNN's)

$89.00
$107.69 incl. vat

duration: 4 hours |

Language: English (US) |

access duration: 90 days |

Details

Graph neural networks (GNNs) have recently become widely applied graph-analysis tools as they help capture indirect dependencies between data elements. This course teaches you about the different tools and functions of these GNNs. Start with an introductory part where you’ll learn how to transform graph data for use in these networks, what the use cases for machine learning in analyzing graph data are, and the challenges around modelling graphs for use in neural networks, including the use of adjacency matrices and node embeddings. In addition, you’ll learn how to build, train, and evaluate a multi-label classification model using a graph convolutional network (GCN) constructed using the Spektral Python library.

Result

After completing this course, you’ll have a good understanding of the functions of Graph Neural Networks. And you’ll be able to classify Graph Nodes with the Spektral Library.

Prerequisites

No formal prerequisites. Some prior knowledge about Graph Neural Networks is recommended.

Target audience

Network Administrator, Software Developer

Content

Graph Neural Networks (GNN's)

4 hours

GNNs: An Introduction to Graph Neural Networks

  • Graph neural networks (GNNs) have recently become
  • widely applied graph-analysis tools as they help capture indirect dependencies between data elements. Take this course to learn how to transform graph data for use in GNNs.
  • Explore the use cases for machine learning in analyzing graph data and the challenges around modeling graphs for use in neural networks, including the use of adjacency matrices and node embeddings. Examine how a convolution function captures the properties of a node and those of its neighbors. While doing so explore normalization concepts, including symmetric normalization of adjacency matrices.
  • Moving along, work with the Spektral Python library to model a graph dataset for application in a GNN. Finally, practice defining a convolution function for a GNN and examine how the resultant message propagation works.
  • Upon completion you'll have a clear understanding of the need for and challenges around using graph data for machine learning and recognize the power of graph convolutional networks (GCNs).

GNNs: Classifying Graph Nodes with the Spektral Library

  • Machine learning (ML) models can be used to extract insights

  • from your graph data. Use this course to learn how to build, train,
  • and evaluate a multi-label classification model using a graph
  • convolutional network (GCN) constructed using the Spektral Python
  • library. Begin by structuring a Spektral dataset for machine
  • learning and learn how data is modeled using an adjacency matrix
  • and feature vectors. Explore how to assign instances of your data
  • to training, validation, and test sets using masks applied to your
  • dataset instance. Construct a graph neural network (GNN) with input
  • layers for the adjacency matrix and features and a GCN
  • convolutional layer and use it to perform node classification.
  • Discover how node features, the edges of the graph, and the
  • structure of the neural network affect the performance of the
  • classification model. Upon completion, you’ll be able to prepare a
  • graph structure for use in an ML model and define the factors which
  • can improve the accuracy of model predictions.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

WHY_ICTTRAININGEN

Via ons opleidingsconcept bespaar je tot 80% op trainingen

Start met leren wanneer je wilt. Je bepaalt zelf het gewenste tempo

Spar met medecursisten en profileer je als autoriteit in je vakgebied.

Ontvang na succesvolle afronding van je cursus het officiële certificaat van deelname van Icttrainingen.nl

Krijg inzicht in uitgebreide voortgangsinformatie van jezelf of je medewerkers

Kennis opdoen met interactieve e-learning en uitgebreide praktijkopdrachten door gecertificeerde docenten

Orderproces

Once we have processed your order and payment, we will give you access to your courses. If you still have any questions about our ordering process, please refer to the button below.

read more about the order process

What is included?

Certificate of participation Yes
Monitor Progress Yes
Award Winning E-learning Yes
Mobile ready Yes
Sharing knowledge Unlimited access to our IT professionals community
Study advice Our consultants are here for you to advice about your study career and options
Study materials Certified teachers with in depth knowledge about the subject.
Service World's best service

Platform

Na bestelling van je training krijg je toegang tot ons innovatieve leerplatform. Hier vind je al je gekochte (of gevolgde) trainingen, kan je eventueel cursisten aanmaken en krijg je toegang tot uitgebreide voortgangsinformatie.

Life Long Learning

Follow multiple courses? Read more about our Life Long Learning concept

read more

Contact us

Need training advise? Contact us!


contact