Machine Learning For Tabular Data
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Machine Learning For Tabular Data
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Author : Mark Ryan
language : en
Publisher: Simon and Schuster
Release Date : 2025-03-04
Machine Learning For Tabular Data written by Mark Ryan and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-04 with Computers categories.
Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline. Machine Learning for Tabular Data will teach you how to: • Pick the right machine learning approach for your data • Apply deep learning to tabular data • Deploy tabular machine learning locally and in the cloud • Pipelines to automatically train and maintain a model Machine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day. Foreword by Antonio Gulli. About the technology Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases and other tabular data sources using gradient boosting, deep learning, and generative AI. About the book Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable. What's inside • Master XGBoost • Apply deep learning to tabular data • Deploy models locally and in the cloud • Build pipelines to train and maintain models About the reader For readers experienced with Python and the basics of machine learning. About the author Mark Ryan is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, Luca Massaron is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books. Table of Contents Part 1 1 Understanding tabular data 2 Exploring tabular datasets 3 Machine learning vs. deep learning Part 2 4 Classical algorithms for tabular data 5 Decision trees and gradient boosting 6 Advanced feature processing methods 7 An end-to-end example using XGBoost Part 3 8 Getting started with deep learning with tabular data 9 Deep learning best practices 10 Model deployment 11 Building a machine learning pipeline 12 Blending gradient boosting and deep learning A Hyperparameters for classical machine learning models B K-nearest neighbors and support vector machines
Modern Deep Learning For Tabular Data
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Author : Andre Ye
language : en
Publisher: Apress
Release Date : 2022-12-27
Modern Deep Learning For Tabular Data written by Andre Ye and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Computers categories.
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 data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data – an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs – Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks – through both their ‘default’ usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability. Each chapter comes with extensive visualization, code, and relevant research coverage. Modern Deep Learning for Tabular Data is one of the first of its kind – a wide exploration of deep learning theory and applications to tabular data, integrating and documenting novel methods and techniques in the field. This book provides a strong conceptual and theoretical toolkit to approach challenging tabular data problems. What You Will Learn Important concepts and developments in modern machine learning and deep learning, with a strong emphasis on tabular data applications. Understand the promising links between deep learning and tabular data, and when a deep learning approach is or isn’t appropriate. Apply promising research and unique modeling approaches in real-world data contexts. Explore and engage with modern, research-backed theoretical advances on deep tabular modeling Utilize unique and successful preprocessing methods to prepare tabular data for successful modelling. Who This Book Is ForData scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security.
Simplifying Data Preparation For Machine Learning On Tabular Data
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Author : Vraj Shah
language : en
Publisher:
Release Date : 2022
Simplifying Data Preparation For Machine Learning On Tabular Data written by Vraj Shah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Machine learning (ML) over tabular data has become ubiquitous with applications in many domains. This success has led to the rise of ML platforms, including automated ML (AutoML) platforms to manage the end-to-end ML workflow. The tedious grunt work involved in data preparation (prep) reduces data scientist productivity and slows down the ML development lifecycle, which makes the automation of data prep even more critical. While many works have looked into feature engineering and model selection in the end-to-end ML workflows, little attention has been paid towards understanding data prep and its utility for ML. Also, automating data prep remains challenging due to several reasons such as semantic gaps and lack of ways to objectively measure accuracy. In this dissertation, we take a step towards addressing such challenges using database schema management and ML techniques to simplify, better automate, and understand the utility of ML data prep. We create new benchmark datasets, methodology for benchmarking and automating ML data prep, and devise novel empirical analyses to characterize the significance of critical data prep steps. Our work presents several critical artifacts that not only provide a systematic approach to reduce grunt work and improve the productivity of ML practitioners but also can help establish the science of building (Auto)ML platforms. Our work opens up several new research directions at the intersection of ML, data management, and ML system design.
A Study On How Data Quality Influences Machine Learning Predictability And Interpretability For Tabular Data
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Author : Humra Ahsan
language : en
Publisher:
Release Date : 2022
A Study On How Data Quality Influences Machine Learning Predictability And Interpretability For Tabular Data written by Humra Ahsan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Electronic data processing categories.
Today data is the most important part of any organization, as data is everywhere around us. Most companies produce large amount of data that is essential for the decision making process. In this context, many machine learning and artificial intelligence methods can be used for analysis and prediction. To understand the data quality and make efficient use of the data, several pre-processing steps are necessary. In various fields of study and industry, machine learning is becoming the dominant problem-solving technique. Machine learning models are now being used to solve a variety of real-world problems in a variety of disciplines, ranging from retail and finance to medicine and healthcare which demands high predictive accuracy. Understanding data quality and feature engineering are some of the most critical parts of any machine learning project. Mostly, companies manage tabular data that needs to be converted into numerical data. However, this improved predictive accuracy has often been achieved through increased model complexity which leads to a lack of transparency. The major disadvantage is that the models' inner workings are hidden from the user because it prevents even an experienced professional from interpreting and understanding the reasoning behind the system and how some decisions are made. The quality and quantity of data used to train machine learning algorithms are directly related to their predicted ability. Quality data leads to accurate predictions that in turn leads to accurate explanations. In many cases, it is important to know how predictions are made. The research is focused on the effect of data quality and feature engineering on training different tabular datasets using different machine learning models and the ranking of features in terms of their importance to the prediction. The results are compared in terms of performance accuracy to find which feature set and which model works best.
Deep Neural Networks And Tabular Data
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Author : Vadim Borisov
language : en
Publisher:
Release Date : 2023
Deep Neural Networks And Tabular Data written by Vadim Borisov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods.
Interpretable Machine Learning And Generative Modeling With Mixed Tabular Data
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Author : Kristin Blesch
language : en
Publisher:
Release Date : 2024
Interpretable Machine Learning And Generative Modeling With Mixed Tabular Data written by Kristin Blesch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Explainable artificial intelligence or interpretable machine learning techniques aim to shed light on the behavior of opaque machine learning algorithms, yet often fail to acknowledge the challenges real-world data imposes on the task. Specifically, the fact that empirical tabular datasets may consist of both continuous and categorical features (mixed data) and typically exhibit dependency structures is frequently overlooked. This work uses a statistical perspective to illuminate the far-reaching implications of mixed data and dependency structures for interpretability in machine learning. Several interpretability methods are advanced with a particular focus on this kind of data, evaluating their performance on simulated and real data sets. Further, this cumulative thesis emphasizes that generating synthetic data is a crucial subroutine for many interpretability methods. Therefore, this thesis also advances methodology in generative modeling concerning mixed tabular data, presenting a tree-based approach for density estimation and data generation, accompanied by a user-friendly software implementation in the Python programming language.
Deep Learning With Structured Data
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Author : Mark Ryan
language : en
Publisher: Simon and Schuster
Release Date : 2020-12-08
Deep Learning With Structured Data written by Mark Ryan and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with Computers categories.
Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory
Tabular Information Extraction From Datasheets With Deep Learning For Semantic Modeling
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Author : Yakup Akkaya
language : en
Publisher:
Release Date : 2022
Tabular Information Extraction From Datasheets With Deep Learning For Semantic Modeling written by Yakup Akkaya and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The growing popularity of artificial intelligence and machine learning has led to the adop- tion of the automation vision in the industry by many other institutions and organizations. Many corporations have made it their primary objective to make the delivery of goods and services and manufacturing in a more efficient way with minimal human intervention. Au- tomated document processing and analysis is also a critical component of this cycle for many organizations that contribute to the supply chain. The massive volume and diver- sity of data created in this rapidly evolving environment make this a highly desired step. Despite this diversity, important information in the documents is provided in the tables. As a result, extracting tabular data is a crucial aspect of document processing. This thesis applies deep learning methodologies to detect table structure elements for the extraction of data and preparation for semantic modelling. In order to find optimal structure definition, we analyzed the performance of deep learning models in different formats such as row/column and cell. The combined row and column detection models perform poorly compared to other models' detection performance due to the highly over- lapping nature of rows and columns. Separate row and column detection models seem to achieve the best average F1-score with 78.5% and 79.1%, respectively. However, de- termining cell elements from the row and column detections for semantic modelling is a complicated task due to spanning rows and columns. Considering these facts, a new method is proposed to set the ground-truth information called a content-focused annota- tion to define table elements better. Our content-focused method is competent in handling ambiguities caused by huge white spaces and lack of boundary lines in table structures; hence, it provides higher accuracy. Prior works have addressed the table analysis problem under table detection and table structure detection tasks. However, the impact of dataset structures on table structure detection has not been investigated. We provide a comparison of table structure detection performance with cropped and uncropped datasets. The cropped set consists of only table images that are cropped from documents assuming tables are detected perfectly. The uncropped set consists of regular document images. Experiments show that deep learning models can improve the detection performance by up to 9% in average precision and average recall on the cropped versions. Furthermore, the impact of cropped images is negligible under the Intersection over Union (IoU) values of 50%-70% when compared to the uncropped versions. However, beyond 70% IoU thresholds, cropped datasets provide significantly higher detection performance.
Tabular Machine Learning On Small Size And High Dimensional Data
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Author : Andrei Margeloiu
language : en
Publisher:
Release Date : 2025
Tabular Machine Learning On Small Size And High Dimensional Data written by Andrei Margeloiu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.