مقدمه‌ای بر Mathematica و Wolfram برای داده‌کاوی: کاربردها در تحلیل داده، یادگیری ماشین و شبکه‌های عصبی (ویرایش دوم)

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You’ll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You’ll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you’ll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language. 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 Mathematica Environment Notebook Interface Text Processing Palettes Notebook Style and Features Expression in Mathematica Assigning Values Built-in Functions Dates and Time Strings Basic Plotting Logical Operators and Infix Notation Algebraic Expressions Solving Algebraic Equations Using Wolfram Alpha Inside Mathematica Delayed and Immediate Expressions Improving Code Code Performance Handling Errors Debugging Techniques How Mathematica Works How Computations are Made (Form of Input) Searching for Assistance Notebook Security Summary 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 Matrixes Matrix Operations Restructuring a Matrix Manipulating Lists Retrieving Data Assigning or Removing Values Structuring List Criteria Selection Summary 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 Joining and Merging Datasets Customizing a Dataset Generalization of Hash Tables Summary Chapter 4: Import and Export Importing Files CSV and TSV Files XLSX Files JSON Files Web Data Semantic Import Quantities Datasets with Quantities Costume Import (Dealing with Large Datasets) Export Other Formats XLS and XLSX Formats JSON Formats Content File Objects Searching Files with Wolfram Language Connecting to External Services External Connections External Resources Database and File Operations (SQL) Summary Chapter 5: Data Visualization Basic Visualization 2D Plots Plotting Data Plotting Defined Functions Customizing Plots Adding Text to Charts Frame and Grids Filled Plots Filling Patterns and Gradient Combining Plots Multiple Plots Multiaxis 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 Summary Chapter 6: Statistical Data Analysis Random Numbers Random Sampling Systematic Sampling Commons Statistical Measures Measures of Central Tendency Measures of Dispersion Statistical Charts Bar Charts Histograms Pie Charts and Sector Charts Box Plots Distribution Chart Charts Palette Ordinary Least Squares Method Pearson Coefficient Linear Fit Model Properties Summary 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 and Querying Descriptive Statistics Table and Grid Formats Dataset Visualization Data Outside Dataset Format 2D and 3D Plots Summary 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 Changing the Distance Function Different k’s Cluster Classify Summary 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 Softmax Layer Function Layer Encoder and Decoders Encoder Pooling Layer Decoders Applying Encoder and Decoders NetChains and Graphs Containers Multiple Chains NetGraphs Combining Containers Network Properties Exporting and Importing a Model Summary Chapter 10: Neural Networks Framework Training a Neural Network Data Input Training Phase Model Implementation Batch Size and Rounds Training Method (NetTrain) 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 MINST Dataset LeNet Architecture MXNet Framework Preparing LeNet LeNet Training LeNet Model Assesment Testing LeNet GPT and LLM Basics A Brief Overview LLM in the Wolfram Language Chat Notebooks Wolfram Prompt Repository LLM Functionalities GTP-1 and GPT-2 Models Final Remarks Index