Modern deep learning for tabular data : novel approaches to common modeling problems /

Deep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Ye, Andre (Autor), Wang, Zian (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Berkeley] : Apress, [2023]
Temas:
Machine learning.
Mathematical models.
Apprentissage automatique.
Machine learning
Mathematical models
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Authors
  • About the Technical Reviewer
  • Acknowledgments
  • Foreword 1
  • Foreword 2
  • Introduction
  • Part I: Machine Learning and Tabular Data
  • Chapter 1: Classical Machine Learning Principles and Methods
  • Fundamental Principles of Modeling
  • What Is Modeling?
  • Modes of Learning
  • Quantitative Representations of Data: Regression and Classification
  • The Machine Learning Data Cycle: Training, Validation, and Test Sets
  • Bias-Variance Trade-Off
  • Feature Space and the Curse of Dimensionality
  • Optimization and Gradient Descent
  • Metrics and Evaluation
  • Mean Absolute Error
  • Mean Squared Error (MSE)
  • Confusion Matrix
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Area Under the Receiver Operating Characteristics Curve (ROC-AUC)
  • Algorithms
  • K-Nearest Neighbors
  • Theory and Intuition
  • Implementation and Usage
  • Linear Regression
  • Theory and Intuition
  • Implementation and Usage
  • Other Variations on Simple Linear Regression
  • Logistic Regression
  • Theory and Intuition
  • Implementation and Usage
  • Other Variations on Logistic Regression
  • Decision Trees
  • Theory and Intuition
  • Implementation and Usage
  • Random Forest
  • Gradient Boosting
  • Theory and Intuition
  • AdaBoost
  • XGBoost
  • LightGBM
  • Summary of Algorithms
  • Thinking Past Classical Machine Learning
  • Key Points
  • Chapter 2: Data Preparation and Engineering
  • Data Storage and Manipulation
  • TensorFlow Datasets
  • Creating a TensorFlow Dataset
  • TensorFlow Sequence Datasets
  • Handling Large Datasets
  • Datasets That Fit in Memory
  • Pickle
  • SciPy and TensorFlow Sparse Matrices
  • Datasets That Do Not Fit in Memory
  • Pandas Chunker
  • h5py
  • NumPy Memory Map
  • Data Encoding
  • Discrete Data
  • Label Encoding
  • One-Hot Encoding
  • Binary Encoding
  • Frequency Encoding
  • Target Encoding
  • Leave-One-Out Encoding
  • James-Stein Encoding
  • Weight of Evidence
  • Continuous Data
  • Min-Max Scaling
  • Robust Scaling
  • Standardization
  • Text Data
  • Keyword Search
  • Raw Vectorization
  • Bag of Words
  • N-Grams
  • TF-IDF
  • Sentiment Extraction
  • Word2Vec
  • Time Data
  • Geographical Data
  • Feature Extraction
  • Single- and Multi-feature Transformations
  • Principal Component Analysis
  • t-SNE
  • Linear Discriminant Analysis
  • Statistics-Based Engineering
  • Feature Selection
  • Information Gain
  • Variance Threshold
  • High-Correlation Method
  • Recursive Feature Elimination
  • Permutation Importance
  • LASSO Coefficient Selection
  • Key Points
  • Part II: Applied Deep Learning Architectures
  • Chapter 3: Neural Networks and Tabular Data
  • What Exactly Are Neural Networks?
  • Neural Network Theory
  • Starting with a Single Neuron
  • Feed-Forward Operation
  • Introduction to Keras
  • Modeling with Keras
  • Defining the Architecture
  • Compiling the Model
  • Training and Evaluation
  • Loss Functions
  • Math Behind Feed-Forward Operation
  • Activation Functions

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