π application of machine learning in traffic optimization
π₯Free recorded course by Powel gora
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
π₯Free recorded course by Powel gora
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
PaweΕ Gora: Applications of machine learning in traffic optimization
I will be talking about possible applications of machine learning in traffic optimization (and in optimizing some other complex processes). I will describe the process of building traffic metamodels by approximating outcomes of traffic simulations using machineβ¦
π4
π Machine Learning with Graphs: Applications of Deep Graph Generation.
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyβ¦
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyβ¦
π5β€1
πGraph Attention Networks
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Machine_Learning #Attention #Neural_Network
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Machine_Learning #Attention #Neural_Network
Baeldung on Computer Science
Graph Attention Networks | Baeldung on Computer Science
Explore graph neural networks that use attention.
π4
π Machine Learning with Graphs: Generative Models for Graphs
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯In this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Generative_Models
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯In this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Generative_Models
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jO8OsE
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generativeβ¦
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generativeβ¦
π6
π Graph Analytics and Graph-based Machine Learning
π₯Free recorded course by Clair Sullivan(Neo4j)
π₯Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
π₯Free recorded course by Clair Sullivan(Neo4j)
π₯Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Graph Analytics and Graph-based Machine Learning
Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between dataβ¦
π4π1
πMachine Learning for Refining Knowledge Graphs: A Survey
π Journal: acm digital library (I.F=14.324)
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
π Journal: acm digital library (I.F=14.324)
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
π₯4π2π1
πΉ Graph Embedding For Machine Learning in Python
π₯In this video, you will learn how to embed graphs into n-dimensional space to use them for machine learning.
π Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph_Embedding #Machine_Learning
π₯In this video, you will learn how to embed graphs into n-dimensional space to use them for machine learning.
π Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph_Embedding #Machine_Learning
YouTube
Graph Embedding For Machine Learning in Python
In this video, we learn how to embed graphs into n-dimensional space to use them for machine learning.
DeepWalk Paper: https://arxiv.org/abs/1403.6652
βΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎ
π Programming Books & Merch π
π The Python Bible Book: https://www.neuralnine.com/books/β¦
DeepWalk Paper: https://arxiv.org/abs/1403.6652
βΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎβΎ
π Programming Books & Merch π
π The Python Bible Book: https://www.neuralnine.com/books/β¦
π5
π Machine Learning with Graphs: Applications of Deep Graph Generation.
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyβ¦
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyβ¦
π1
π Machine Learning with Graphs - Node Embeddings
π₯SDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
π₯SDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
YouTube
Machine Learning with Graphs - Node Embeddings
SDML is partnering with Houston Machine Learning on a series about machine learning with graphs. The content will be mainly based on the Stanford course: https://web.stanford.edu/class/cs224w/
Series schedule:
Introduction; Machine Learning for Graphs
Traditionalβ¦
Series schedule:
Introduction; Machine Learning for Graphs
Traditionalβ¦
π3π2
πGraph-Based Data Science, Machine Learning, and AI
π₯Technical Paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #AI #Data_Science #Machine_Learning
π₯Technical Paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #AI #Data_Science #Machine_Learning
DZone
Graph-Based Data Science, Machine Learning, and AI
What does graphing have to do with machine learning and data science? A lot, actually β learn more in The Year of the Graph Newsletter's Spring 2021 edition.
β€3π2