دانلود کتاب Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks

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Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Introduction to Mathematica
Why Mathematica?
The Wolfram Language
Structure of Mathematica
Design of Mathematica
Notebooks
Text Processing
Palettes
Expression in Mathematica
Assigning Values
Built-in Functions
Dates and Time
Basic Plotting
Logical Operators and Infix Notation
Algebraic Expressions
Solving Algebraic Equations
Using Wolfram Alpha Inside Mathematica
Delayed Expressions
Code Performance
Strings
How Mathematica Works
How Computations are Made (Form of Input)
Searching for Assistance
Handling Errors
Notebook Security
Chapter 2: Data Manipulation
Lists
Types of Numbers
Working with Digits
A Few Mathematical Functions
Numeric Function
Lists of Objects
List Representation
Generating Lists
Arrays of Data
Nested Lists
Vectors
Matrices
Matrix Operations
Restructuring a Matrix
Manipulating Lists
Retrieving Data
Assigning or Removing Values
Structuring List
Criteria Selection
Chapter 3: Working with Data and Datasets
Operations with Lists
Arithmetic Operations to a List
Addition and Subtraction
Division and multiplication
Exponentiation
Joining a list
Applying Functions to a List
Defining Own Functions
Pure Functions
Indexed Tables
Tables with the Wolfram Language
Associations
Dataset Format
Constructing Datasets
Accessing Data in a Dataset
Adding Values
Dropping Values
Filtering Values
Applying Functions
Functions by Column or Row
Customizing a Dataset
Generalization of Hash Tables
Chapter 4: Import and Export
Importing Files
CSV and TSV Files
XLSX Files
JSON Files
Web Data
Semantic Import
Quantities
Datasets with Quantities
Costume Imports
Export
Other Formats
XLS and XLSX Formats
JSON Formats
Content File Objects
Searching Files with Wolfram Language
Chapter 5: Data Visualization
Basic Visualization
2D Plots
Plotting Data
Plotting Defined Functions
Customizing Plots
Adding Text to Charts
Frame and Grids
Filled Plots
Combining Plots
Multiple Plots
Coloring Plot Grids
Colors Palette
3D Plots
Customizing 3D Plots
Hue Color Function and List3D
Contour Plots
3D Plots and 2D Projections
Plot Themes
Chapter 6: Statistical Data Analysis
Random Numbers
Random Sampling
Systematic Sampling
Common Statistical Measures
Measures of Central Tendency
Measures of Dispersion
Statistical Charts
BarCharts
Histograms
Pie Charts and Sector Charts
Box Plots
Distribution Chart
Charts Palette
Ordinary Least Square Method
Pearson Coefficient
Linear Fit
Model Properties
Chapter 7: Data Exploration
Wolfram Data Repository
Wolfram Data Repository Website
Selecting a Category
Extracting Data from the Wolfram Data Repository
Accessing Data Inside Mathematica
Data Observation
Descriptive Statistics
Table and Grid Formats
Dataset Visualization
Data Outside Dataset Format
2D and 3D Plots
Chapter 8: Machine Learning with the Wolfram Language
Gradient Descent Algorithm
Getting the Data
Algorithm Implementation
Multiple Alphas
Linear Regression
Predict Function
Boston Dataset
Model Creation
Model Measurements
Model Assessment
Retraining Model Hyperparameters
Logistic Regression
Titanic Dataset
Data Exploration
Classify Function
Testing the Model
Data Clustering
Clusters Identification
Choosing a Distance Function
Identifying Classes
K-Means Clustering
Dimensionality Reduction
Applying K-Means
Chaining the Distance Function
Different K’s
Cluster Classify
Chapter 9: Neural Networks with the Wolfram Language
Layers
Input Data
Linear Layer
Weights and Biases
Initializing a Layer
Retrieving Data
Mean Squared Layer
Activation Functions
SoftmaxLayer
Encoders and Decoders
Encoders
Pooling Layer
Decoders
Applying Encoders and Decoders
NetChains and Graphs
Containers
Multiple Chains
NetGraphs
Combining Containers
Network Properties
Exporting and Importing a Model
Chapter 10: Neural Network Framework
Training a Neural Network
Data Input
Training Phase
Model Implementation
Batch Size and Rounds
Training Method
Measuring Performance
Model Assessment
Exporting a Neural Network
Wolfram Neural Net Repository
Selecting a Neural Net Model
Accessing Inside Mathematica
Retrieving Relevant Information
LeNet Neural Network
LeNet Model
MNIST Dataset
LeNet Architecture
MXNet Framework
Preparing LeNet
LeNet Training
LeNet Model Assessment
Testing LeNet
Final Remarks
Appendix A
Installing Mathematica
Index