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The Palindrome

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Matrices and graphs
The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices
Jan 9, 2023 • 
Tivadar Danka
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Matrices and graphs
25
The Camel Principle
What it is, how it works, and why it is essential
May 5 • 
Tivadar Danka
151

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The Camel Principle
24
Vectorization in theory
Making expressions do more, faster
May 1, 2024 • 
Tivadar Danka
66

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Vectorization in theory
5
Machine Learning From Zero is ready to go!
The open source neural-networks-from-scratch book you were all waiting for
Oct 13, 2024 • 
Tivadar Danka
57

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Machine Learning From Zero is ready to go!
5
Can Large Language Models replace mathematicians?
Exploring the mathematical thinking of humans and LLMs
Apr 27 • 
Tivadar Danka
51

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Can Large Language Models replace mathematicians?
8
Introduction to Computational Graphs
Neural Networks From Scratch, Part I
May 14 • 
Tivadar Danka
39

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Introduction to Computational Graphs
4
How large that number in the Law of Large Numbers is?
All large numbers are large, but some are larger than others
Sep 4, 2023 • 
Tivadar Danka
 and 
Levi
41

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How large that number in the Law of Large Numbers is?
The Derivatives of Computational Graphs
Forward-mode differentiation and its shortcomings
May 30 • 
Tivadar Danka
37

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The Derivatives of Computational Graphs
How to grow a decision tree
Beating neural networks one if-else condition at a time
Oct 26, 2024 • 
Tivadar Danka
35

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How to grow a decision tree
4
The Computer Scientist's Guide to Graph Theory, ep. 03
Eulerian and Hamiltonian tours
Jan 8, 2024 • 
Tivadar Danka
 and 
Alejandro Piad Morffis
39

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The Computer Scientist's Guide to Graph Theory, ep. 03
The Maximum Likelihood Estimation
(is the foundation of machine learning)
Jan 2, 2024 • 
Tivadar Danka
37

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The Maximum Likelihood Estimation
Vectorization in practice
Making data structures do less, faster
May 14, 2024 • 
Tivadar Danka
37

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Vectorization in practice
2
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