Deep Learning For Structured Data
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Deep Learning with Structured Data
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
Deep Learning with Structured Data
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
Deep Learning for Structured Data
Modern machine learning excels at modeling statistical associations and distributions of observational data, showing strong performance across many tasks. However, the application of ML algorithms faces two distinct challenges: (1) in many real-world data-driven applications, collecting sufficient amount of high-quality labeled data remains as a bottleneck, which is challenging and expensive. Particularly, the widely adopted crowd-sourced data collection pipeline inevitably involves human labelers of varying expertise, which further complicate the label quality. (2) One another challenge is their lack of interpretability. Deep models are known as black boxes, which hinders practitioners' trust in applying them to high-stack applications like healthcare or finances. Motivated by these two problems, in this dissertation, I focus on the improvement of deep learning from these two directions, weakly-supervised learning and model interpretability. Particularly, I focus on structured data, including (1) relational data (graphs), which is a powerful tool to depict non-Euclidean data forms using nodes representing entities and edges modeling relations, and (2) sequential decision processes, which contains a sequence of state-action pairs. Both data forms exist pervasively in many real-world applications, like social networks, protein structures, autonomous driving, etc. In this dissertation, I will introduce some of my representative works addressing these two challenges. In the first two works, I will introduce the extension of GNNs for semi-supervised learning and imbalanced labels. Then, I will present a self-supervised disambiguated learning for more discriminative representation learning on graphs. Followed by them, I will present two works in learning sequential decision-making agents. First is how to imitate decision-making skills from human demonstrations of varying qualities by discovering a set of skills, with each skill to model a different action primitive. A well-performed agent can be obtained by composing those skills hierarchically. Last, I will discuss the strategy of designing a more interpretable neural agent, which can explicitly present its learned knowledge in the form of causal graphs.