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LiveLessons - Calculus for Machine Learning (2021)

Posted By: ParRus

Date: 24 Jan 2021 17:50:43

LiveLessons - Calculus for Machine Learning (2021)

Much of machine learning is built around the idea of loss functions and optimizing for them. To understand them, you first need to understand Calculus.

Table of Contents

1 Calculus for Machine Learning LiveLessons (Video Training) – Introduction
2 Topics
3 1.1 Differential versus Integral Calculus
4 1.2 A Brief History
5 1.3 Calculus of the Infinitesimals
6 1.4 Modern Applications
7 Topics
8 2.1 Continuous versus Discontinuous Functions
9 2.2 Solving via Factoring
10 2.3 Solving via Approaching
11 2.4 Approaching Infinity
12 2.5 Exercises
13 Topics
14 3.1 Delta Method
15 3.2 The Most Common Representation
16 3.3 Derivative Notation
17 3.4 Constants
18 3.5 Power Rule
19 3.6 Constant Product Rule
20 3.7 Sum Rule
21 3.8 Exercises
22 Topics
23 4.1 Product Rule
24 4.2 Quotient Rule
25 4.3 Chain Rule
26 4.4 Exercises
27 4.5 Power Rule on a Function Chain
28 Topics
29 5.1 Introduction
30 5.2 Autodiff with PyTorch
31 5.3 Autodiff with TensorFlow
32 5.4 Directed Acyclic Graph of a Line Equation
33 5.5 Fitting a Line with Machine Learning
34 Topics
35 6.1 Derivatives of Multivariate Functions
36 6.2 Partial Derivative Exercises
37 6.3 Geometrical Examples
38 6.4 Geometrical Exercises
39 6.5 Notation
40 6.6 Chain Rule
41 6.7 Chain Rule Exercises
42 Topics
43 7.1 Single-Point Regression
44 7.2 Partial Derivatives of Quadratic Cost
45 7.3 Descending the Gradient of Cost
46 7.4 Gradient of Mean Squared Error
47 7.5 Backpropagation
48 7.6 Higher-Order Partial Derivatives
49 7.7 Exercise
50 Topics
51 8.1 Binary Classification
52 8.2 The Confusion Matrix and ROC Curve
53 8.3 Indefinite Integrals
54 8.4 Definite Integrals
55 8.5 Numeric Integration with Python
56 8.6 Exercises
57 8.7 Finding the Area Under the ROC Curve
58 8.8 Resources for Further Study of Calculus

also You can find my other helpful Programming-posts
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