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Introduction to computational graphs
Laying the groundwork of our custom neural network framework
May 23
•
Tivadar Danka
42
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Introduction to computational graphs
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Vectorization in practice
Making data structures do less, faster
May 14
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Tivadar Danka
35
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Vectorization in practice
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2
Understanding k-Nearest Neighbors
Show me your neighbors, I’ll tell you who you are
May 9
•
Tivadar Danka
and
Levi
35
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Understanding k-Nearest Neighbors
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Vectorization in theory
Making expressions do more, faster
May 1
•
Tivadar Danka
45
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Vectorization in theory
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Linear Regression and The Exploding Gradients
Even simple models can produce huge problems
Apr 17
•
Tivadar Danka
4
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Linear Regression and The Exploding Gradients
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Linear regression in multiple variables
(finally) entering higher dimensions
Mar 5
•
Tivadar Danka
7
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Linear regression in multiple variables
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The mathematics of optimization for deep learning
A brief guide to minimize a function with millions of variables
Feb 12
•
Tivadar Danka
19
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The mathematics of optimization for deep learning
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Building an interface for machine learning
Laying the foundations of our handmade machine learning library
Jan 22
•
Tivadar Danka
20
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Building an interface for machine learning
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5
Logistic regression
(is a classification algorithm)
Jan 17
•
Tivadar Danka
24
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Logistic regression
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The Maximum Likelihood Estimation
(is the foundation of machine learning)
Jan 2
•
Tivadar Danka
34
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The Maximum Likelihood Estimation
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Where does the mean squared error come from?
Understanding the most important loss function from a statistical perspective
Dec 21, 2023
•
Tivadar Danka
2
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Where does the mean squared error come from?
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Linear regression and the least squares problem
Optimizing linear regression by hand
Dec 12, 2023
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Tivadar Danka
2
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Linear regression and the least squares problem
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