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

• Record Nr.: UNINA9910872191103321
• Autore: Villalobos Alva Jalil
• Pubbl/distr/stampa: Berkeley, CA : , : Apress L. P., , 2024; ©2024
• ISBN: 9798868803482; 9798868803475
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (476 pages)
• Disciplina: 001.42
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• 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.