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Study mode:
on
1
Intro
2
Neural networks and symbolic reasoning
3
Problem statement
4
Generating expressions randomly
5
Counting number of expressions
6
Formulae for counting no. of trees and expressions
7
Generating dataset for Integration
8
Ordinary Differential Equations (1st order)
9
Dataset Cleaning
10
Dataset Statistics
11
Sequence Decoding
12
Evaluation
13
Comparison with mathematical frameworks
14
Generalization across generators
15
Generalization beyond the generator
16
Conclusion
Description:
Explore the intersection of deep learning and symbolic mathematics in this 55-minute video from Launchpad. Delve into neural networks and symbolic reasoning, learn about generating random expressions, and understand formulae for counting trees and expressions. Discover techniques for creating datasets for integration and ordinary differential equations, followed by dataset cleaning and statistics. Examine sequence decoding, evaluation methods, and comparisons with mathematical frameworks. Investigate generalization across and beyond generators, gaining insights into the potential of deep learning in symbolic mathematical problem-solving.

Deep Learning for Symbolic Mathematics

Launchpad
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