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Enhancing Neural Networks with Logic Based Rules

Data Science Dojo via YouTube

Overview

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Explore how to build smarter, more transparent AI systems by combining neural networks with logic-based rules in this 41-minute webinar. Discover the limitations of traditional neural networks and learn why neuro-symbolic AI represents a crucial advancement in artificial intelligence development. Examine the key differences between symbolic reasoning and deep learning approaches, understanding their respective strengths and weaknesses. Delve into real-world applications across healthcare and finance industries, seeing how logic-driven models enhance AI decision-making, transparency, and control. Follow along with a comprehensive live demonstration of fraud detection using neuro-symbolic AI techniques, complete with results comparison and analysis. Gain practical insights into system architecture design and access resources to implement neuro-symbolic AI in your own projects. Participate in an interactive Q&A session to deepen your understanding of this emerging field that bridges traditional symbolic reasoning with modern deep learning capabilities.

Syllabus

00:00 – Introduction
01:20 – The Problem with Neural Networks
03:30 – Strengths & Weaknesses of Neural vs. Symbolic AI
05:30 – Why Neuro-Symbolic AI Matters
07:20 – System Architecture Overview
10:00 – Use Cases Healthcare & Finance
12:30 – Live Demo: Fraud Detection with Neuro-Symbolic AI
30:00 – Results & Comparison
35:00 – Q&A + Closing Remarks

Taught by

Data Science Dojo

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