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

• Autore: Villalobos Alva Jalil
• Pubblicazione: Berkeley, CA : , : Apress L. P., , 2024; ©2024
• Edizione: 2nd ed.
• Descrizione fisica: 1 online resource (476 pages)
• Disciplina: 001.42
• Soggetto topico: Mathematica (Computer program language); Wolfram language (Computer program language); Mathematics - Data processing; Artificial intelligence
• Note generali: Includes index.
• Nota di contenuto: Intro -- 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Beginning Mathematica and Wolfram for Data Science
• ISBN: 9798868803482; 9798868803475
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910872191103321
• Lo trovi qui: Univ. Federico II