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Top Tracks
1
Awesome song and introduction
2
Intro
3
Review of fitting a line to data
4
Histograms and conditional probabilities
5
Basic decision tree concepts
6
Motivation for MLE
7
Overview of the Normal Distribution
8
Running data through a recurrent neural network
9
Review of concepts
10
Basic anatomy of a recurrent neural network
11
Building a tree with Gini Impurity
12
Thinking about where to center the distribution
13
Motivation for Regression Trees
14
Regression Trees vs Classification Trees
15
Why Naive Bayes is Naive
16
Bootstrapping in action!
17
Numeric and continuous variables
18
Pseudocounts
19
Building a Regression Tree with one variable
20
Summary
21
Summary of concepts and main ideas
22
Classification example
23
Underflow and Log function
24
Word Embedding
25
Using MLE to find the optimal location for the center
26
Introduction
27
Uniform Distribution
28
Exponential Distribution
29
Adding branches
30
Practical implications
31
Shared weights and biases
32
Calculating surprise for a series of events
33
AdaBoost, Clearly Explained
34
Positional Encoding
35
Likelihood
36
Bootstrapping defined
37
Measuring the Fit
38
Equation for surprise
39
How to prevent overfitting
40
Description of Neural Networks
41
Introduction to surprise
42
Creating a squiggle from curved lines
43
A silly example of classification
44
Background
45
Calculating standard errors and confidence intervals with bootstrapping
46
Least Squares
47
What is Multiple Regression
48
StatQuest: Principal Component Analysis (PCA), Step-by-Step
49
Entropy defined for a coin
50
Classifying samples with logistic regression
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