Home Page Icon
Home Page
Table of Contents for
Cover
Close
Cover
by Hadrien Jean
Essential Math for Data Science
1. Basic Algebra
1.1 Variables
1.1.1 From Computer Programming to Calculus
1.1.2 Unknowns
1.1.3 Dependent And Independent Variables
1.2 Equations And Inequalities
1.2.1 Equations
1.2.2 Inequalities
1.2.3 Hands-On Project: Paris Apartments
1.3 Functions
1.3.1 From Equations To Functions
1.3.2 Computer Programming And Mathematical Functions
1.3.3 Nonlinear Functions
1.3.4 Inverse Function
1.3.5 Hands-On Project: Activation Function
2. Math On The Cartesian Plane
2.1 Coordinates And Vectors
2.1.1 Geometric Vectors
2.1.2 Coordinate Vectors
2.1.3 Hands-On Project: Images As Model Inputs
2.2 Distance formula
2.2.1 Definitions
2.2.2 Hands-On Project: k-Nearest Neighbors
2.3 Graphical Representation of Equations And Inequalities
2.3.1 Intuition
2.3.2 How To Plot Equations
2.3.3 Solving Equations Graphically
2.3.4 Inequalities
2.4 Slope And Intercept
2.4.1 Slope
2.4.2 Intercept
2.5 Nonlinear functions
2.5.1 Definition
2.5.2 Function Shape
2.6 Hands-On Project: MSE Cost Function With One Parameter
2.6.1 Cost function
2.6.2 Mathematical Definition of the Cost Function
2.6.3 Implementation
3. Calculus
3.1 Derivatives
3.1.1 Insights
3.1.2 Mathematical Definition of Derivative
3.1.3 Derivatives of Linear And Nonlinear Functions
3.1.4 Derivative Rules
3.1.5 Hands-On Project: Derivative Of The MSE Cost Function
3.2 Integrals And Area Under The Curve
3.2.1 Insights
3.2.2 Mathematical Definition
3.2.3 Hands-On Project: The ROC Curve
3.3 Partial Derivatives And Gradients
3.3.1 Partial Derivatives
3.3.2 Gradient
3.4 Hands-On Project: MSE Cost Function With Two Parameters
3.4.1 The Cost Function
3.4.2 Partial Derivatives
4. Scalars and Vectors
4.1 Introduction
4.1.1 Vector Spaces
4.1.2 Coordinate Vectors
4.2 Special Vectors
4.2.1 Unit Vectors
4.2.2 Basis Vectors
4.2.3 Zero Vectors
4.2.4 Row and Columns Vectors
4.2.5 Orthogonal Vectors
4.3 Operations and Manipulations on Vectors
4.3.1 Scalar Multiplication
4.3.2 Vector Addition
4.3.3 Using Addition and Scalar Multiplication
4.3.4 Transposition
4.3.5 Operations on Other Vector Types - Functions
4.4 Norms
4.4.1 Definitions
4.4.2 Examples of Norms
4.4.3 Norm Representations
4.5 The Dot Product with vectors
4.5.1 Definition
4.5.2 Geometric interpretation
4.5.3 Properties
4.5.4 Hands-on Project: Vectorizing the Squared L 2 Norm with the Dot Product
4.6 Hands-on Project: Regularization
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Next
Next Chapter
Essential Math for Data Science
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset