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Study mode:
on
1
Intro
2
Towards a Problem Statement
3
Datalog Ontologies: Example
4
RRN Model: Intuitions
5
RRN Learning: Intuitions
6
RRN Learning: Overview
7
RRN Prediction: Intuition
8
A Deeper Dive: Setup
9
A Deeper Dive: Model
10
Gated Recurrent Units (GRUS)
11
Algorithm 1: Generating individual embeddings
12
A Deeper Dive: Prediction
13
A Deeper Dive: Training
14
Algorithm 2: RRN Training
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the second part of a comprehensive lecture on Deep Ontological Networks, focusing on the reasoning process. Delve into advanced concepts presented by Professor Gerardo Simari from UNS, Argentina. Learn about Datalog Ontologies, the RRN (Relational Reasoning Network) model, its learning and prediction processes, and Gated Recurrent Units (GRUs). Examine detailed algorithms for generating individual embeddings and RRN training. Access accompanying slides and a published paper in JAIR for further study. Gain valuable insights into this cutting-edge area of artificial intelligence, combining symbolic methods and deep learning, as part of the Neuro Symbolic Channel's content derived from Arizona State University's AI course.

Deep Ontological Networks: Reasoning Process - Part 2

Neuro Symbolic
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