Graph Neural Networks: Basics, Codes and Simulations for AI
Basics GNNs, GNN Explainer & PyNeuraLogic through 100 + Resources: Code Implementations in Python (StellarGraph & PyG)
Note: 4.0/5 (87 notes) 13,430 students
Instructor(s): Junaid Zafar
Last update: 2022-07-08
What you’ll learn
- Fundamentals Graph AI using Internet of Behaviors
- Basics and implementation of Graph Neural Networks
- How to a create a Graph Neural Network, its training, optimization and testing
- AI Graph feature learning and prediction using FastGCN, gated and mixed grain architectures.
- How to derive an AI sub- graph from Graph Neural Networks
- How create a Graph AI model?
- No prior experience in programming is required. You will learn everything you need to know from the very basics
Graph AI carries immense potential for us to explore, connect the dots and build intelligent applications using the Internet of Behaviors (IoB). Many Graph Neural Networks achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their area to the students. The purpose of this course is to unfold the basics to the cutting-edge concepts and technologies in this realm.
Graphs are all around us; real-world objects are often defined in terms of their connections to other things. A set of objects, and the connections between them, are naturally expressed as a Graph Neural Network (GCN). Recent developments have increased their capabilities and expressive power. They have profound applications in the realm of AI, fake news detection, traffic prediction to recommendation systems.
This course explores and explains modern AI graph neural networks. In this course, we look at what kind of data is most naturally phrased as a graph, and some common examples. Then we explore what makes graphs different from other types of data, and some of the specialized choices we have to make when using graphs. We then build a modern GNN, walking through each of the parts of the model and gradually to state-of-the-art AI GNN models. Finally, we provide a GNN playground where you can play around with a real-world task and dataset to build a stronger intuition of how each component of an AI GNN model contributes to the predictions it makes.
The topics of this course include:
1. Introduction to Graph Machine Learning.
2. Internet of Behaviors.
3. Homographic Intelligence.
4. Graphs Basics and Eigen Centrality.
4. Graph Neural Networks.
5. Graph Attention Networks.
6. Building a Graph Neural Network
7. GNNs Predictors by Pooling Information.
8. Graph AI and its code implementations in Python.
9. Multi- Graphs and Hyper- Graphs in AI using IoB.
10. Design Space for a GNNs.
11. Inductive Biases in GNNs.
12. Pytorch Geometric Implementations.
13. Node2Vec Feature Learning.
14. FAST GCNs.
15. Gated Graph RNNs.
16. Graph LSTMs
17. Mixed Grain Aggregators.
18. Multimodal Graph AI.
19. 100+ Resources on Graph Neural Networks
Who this course is for
- Beginner and intermediate learners in data science, machine learning and artificial intelligence
- Research Students in the realm of data science, big data analytics, Neural Networks and Artificial Intelligence
- Introduction to Graph Neural Networks
- Graph Neural Networks and Internet of Behaviors
- Introduction to Graphs as Discrete Structures
- Graph Neural Networks as Relational Indices
- Embedding in Nodes & Edges- GCNs
- Graph Embeddings
- Geometric Graph Learning
- Geometric Graph Learnings
- Why Graph Neural Networks are Important?
- GNNs Significance
- Taxonomy of GNNs
- Methods for GNNs
- OBE & PyG Implementation of GNNs
- OBE & PyG for Implementation of GNNs
- How to model Images and Text Data as Graph Neural Networks
- Graph Neural Network, Graph neural training and implemention of different layers
- How Attention and Multi-head Attentions works in GNNs?
- Graph AI using self and multi attention neural networks
- How the Graph AI aggregates information from the previous layers
- Semi- Supervised Learning with Graph Neural Networks
- How to build and implement Graph Neural Network in Keras and TensorFlow
- GraphSage- Graph Neural Network with enhanced abilities
- PinSage- Graph Neural Networks
- Optimizing graph hyperparameters and backpropagation mechanism
- GraphSage: Transforming Graph Neural Networks
- PyNeuraLogic & GNN Explainer
- PyNeuraLogic GNN Explainer
- Anti Money Laundering using GNNs
- Graph Neural Networks for Anti- Money Laundering
- GNNs for Identity Inference on Blockchain
- Identity Inference via GNNs
- Graph Embedding in Deep Neural Networks
- To learn link prediction Node, label prediction Graph embedding in Python
- Stellar Graph Library of Python
- How StellarGraph integrates smoothly with Pandas and TensorFlow
- Transfer Learning in Graph AI
- Transfer Learning in Deep Neural Graphs using Python- GRAPHSAGE
- GNNs for Recommender Systems
- Recommender Systems using GNNs
- Node2Vec Feature Learning in Graphs
- Node2Vec Learning in GNNs
- Graph Web Application using Python
- To implement graph web application in python
- Optimization of Graph Neural Networks
- Optimization of Graph Neural Networks
- Graph Mining- Large Scale AI Graphs
- Graph Mining
- Game Theory & AI
- Game Theory & AI
- Graph Generative Adversarial Network Algorithm and its Implementation
- FASTGCN and its implementation in Python
- FAST GCN for Graph Neural Networks
- Mixed Grain Aggregators for Graph Neural Networks
- 100 + Resources on Graph Neural Networks and their Applications
- Graph Neural Networks: 100+ Resources
- Human Augmentation and BCI
- GNNs in Neuromorphic Computing
- Neurotransmitters and Neuromodulation in Neuromorphic Computing
- Graph Neural Networks in Healthcare
- How Neuromorphic computing is used in healthcare domain
- Meta- Learning using GNNs
- Meta- Learning using Graph Neural Networks
- Graph Neural Trasformers
Time remaining or 906 enrolls left
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