Evolution of Graph Algorithms – Benefits and Challenges

Description

Graphs are the most native format to represent the data, it has the built-in structure to store the contextual information and enable data analysts to get better answers for their queries, data engineers to easily extend their ecosystem, and data scientists to build enhanced models. 

With Graphs, we can make better-informed decisions. In this presentation, you will learn about the evolution of Graphs with a review on knowledge graphs, path-finding, clustering, and recommendation algorithms, how they are built, and their use cases. 

Also, we will review the challenges we face as processing more data comes with a cost, and how they are solved in different faces of the machine learning lifecycle.

Join us for a 1-hour tour of Graph.

Key Topics

• Historical timeline of Graph

• The use cases for multiple industries

• Knowledge graphs, path-finding algorithms, clustering, and recommendation algorithms

• Graph Neural Networks

• The challenges with GNNs, sampling, calculating loss functions, embeddings.

Target Audience

• Data Scientists

• Machine Learning Engineers

• Data Analysts

• Data Architects

• Data Tech Leads

Level

Beginner - Intermediate

Prerequisite Knowledge

None

You need an access pass to attend this session: Diversity Access Pass or Full Access Pass apply