Deep Learning With Structured Data
DOWNLOAD
Download Deep Learning With Structured Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Deep Learning With Structured Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Deep Learning With Structured Data
DOWNLOAD
Author : Mark Ryan
language : en
Publisher: Manning
Release Date : 2020-12-29
Deep Learning With Structured Data written by Mark Ryan and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-29 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
Deep Learning For Structured Data
DOWNLOAD
Author : Tianxiang Zhao
language : en
Publisher:
Release Date : 2024
Deep Learning For Structured Data written by Tianxiang Zhao 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.
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.
Deep Learning On Semi Structured Data And Its Applications To Video Game Ai
DOWNLOAD
Author : William Woof
language : en
Publisher:
Release Date : 2020
Deep Learning On Semi Structured Data And Its Applications To Video Game Ai written by William Woof and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Empowering Deep Learning With Graphs
DOWNLOAD
Author : Jiaxuan You (Machine learning researcher)
language : en
Publisher:
Release Date : 2021
Empowering Deep Learning With Graphs written by Jiaxuan You (Machine learning researcher) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Deep learning has reshaped the research and applications in artificial intelligence. Modern deep learning models are primarily designed for regular-structured data, such as sequences and images. These models are built for tasks that take these regular-structured data as the input (e.g., classification, regression), as the output (e.g., generation), or as the structural prior (e.g., neural architecture design). However, not all forms of data are regular-structured. One notable example is graph-structured data, a general and powerful data structure that represents entities and their relationships in a succinct form. While graph-structured data is ubiquitous throughout the natural and social sciences, its discrete and non-i.i.d. nature brings unique challenges to modern deep learning models. In this thesis, we aim to empower deep learning with graph-structured data, by facilitating deep learning models to take graphs as the input, the output, and the prior. My research in these three directions has opened new frontiers for deep learning research: (1) Learning from graphs with deep learning. We develop expressive and effective deep learning methods that can take graphs as the input, which promotes the learning and understanding of graphs. (2) Generation of graphs with deep learning. We formulate the generation process of graphs using deep learning models, which advances the discovery and design of graphs. (3) Graph as the prior for deep learning. We discover that graph structure can serve as a powerful prior for neural architectures and machine learning tasks, which opens a new direction for the design and understanding of deep learning. Finally, we discuss the wide applications of the above-mentioned techniques, including recommender systems, drug discovery, neural architecture design, and missing data imputation.
Machine Learning Pocket Reference
DOWNLOAD
Author : Matt Harrison
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-08-27
Machine Learning Pocket Reference written by Matt Harrison and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-27 with Computers categories.
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Approaches And Applications Of Deep Learning In Virtual Medical Care
DOWNLOAD
Author : Jhanjhi, Noor Zaman
language : en
Publisher: IGI Global
Release Date : 2022-02-25
Approaches And Applications Of Deep Learning In Virtual Medical Care written by Jhanjhi, Noor Zaman and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-25 with Computers categories.
The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students.
The Shape Of Data
DOWNLOAD
Author : Colleen M. Farrelly
language : en
Publisher: No Starch Press
Release Date : 2023-09-12
The Shape Of Data written by Colleen M. Farrelly and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-12 with Computers categories.
This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application. Whether you’re a mathematician, seasoned data scientist, or marketing professional, you’ll find The Shape of Data to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning. This book’s extensive collection of case studies (drawn from medicine, education, sociology, linguistics, and more) and gentle explanations of the math behind dozens of algorithms provide a comprehensive yet accessible look at how geometry shapes the algorithms that drive data analysis. In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you’ll explore: Supervised and unsupervised learning algorithms and their application to network data analysis The way distance metrics and dimensionality reduction impact machine learning How to visualize, embed, and analyze survey and text data with topology-based algorithms New approaches to computational solutions, including distributed computing and quantum algorithms
Mc Microsoft Certified Azure Ai Fundamentals Study Guide
DOWNLOAD
Author : Adora Nwodo
language : en
Publisher: John Wiley & Sons
Release Date : 2025-10-30
Mc Microsoft Certified Azure Ai Fundamentals Study Guide written by Adora Nwodo and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-30 with Computers categories.
A fast and efficient prep handbook for the Azure AI Fundamentals certification exam In MC Microsoft Certified Azure AI Fundamentals Study Guide: Exam AI-900, experienced software engineer Adora Nwodo walks you through every technical topic you need to understand to succeed on the AI-900 certification exam and build a fundamental understanding of Azure AI features. The Study Guide uses the proven and popular Sybex approach to help you use Azure AI in the real-world, whether you're in a technical or non-technical role. Nwodo offers clear explanations, step-by-step instructions, and visual aids to guide you through essential AI concepts and shows you how to use them in the Azure cloud. You'll learn about: Artificial intelligence concepts and workloads Responsible AI use in Azure Core concepts in AI models and solutions Introduction to machine learning concepts, including data preprocessing, training and validation, algorithms, and more The application of machine learning concepts on the Azure cloud platform Computer vision in Azure Natural language processing concepts Generative AI concepts The Azure OpenAI Service Example AI use cases and industry-specific applications The MC Microsoft Certified Azure AI Fundamentals Study Guide highlights best practices for industry newcomers and veterans alike and builds the confidence you need to pass the AI-900 certification exam on your first attempt. Inside the book: Intuitively organized material and additional sample questions that allow you to selectively study the areas in which you need to improve Carefully explained content appropriate for non-technical professionals who interact with AI technologies occasionally or on a daily basis Complimentary access to the superior Sybex online learning environment and test bank, including practice test questions, digital flashcards, and a glossary of key terms Perfect for everyone preparing for the AI-900 certification exam, the Microsoft Certified Azure AI Fundamentals Study Guide is also a must-read for technical and non-technical professionals—especially those working in AI-impacted industries like sales and marketing—who wish to expand their AI skillset and improve their effectiveness at work.
Untitled
DOWNLOAD
Author :
language : en
Publisher: Simon and Schuster
Release Date : 2025-03-04
Untitled written by 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.
N/A N/A
Cognitive Computing Models In Communication Systems
DOWNLOAD
Author : Budati Anil Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-10
Cognitive Computing Models In Communication Systems written by Budati Anil Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-10 with Computers categories.
COGNITIVE COMPUTING MODELS IN COMMUNICATION SYSTEMS A concise book on the latest research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, AI-based devices, computer technology, and their solutions. The book provides a comprehensive overview of state-of-the-art research work on cognitive models in communication systems and computing techniques. It also bridges the gap between various communication systems and solutions by providing the current models and computing techniques, their applications, the strengths and limitations of the existing methods, and the future directions in this area. The contributors showcase their latest research work focusing on the issues, challenges, and solutions in the field of data transmission techniques, computational algorithms, artificial intelligence (AI)-based devices, and computing techniques. Readers will find in this succinctly written and unique book: Topics covering the applications of advanced cognitive devices, models, architecture, and techniques. A range of case studies and applications that will provide readers with the tools to apply cutting-edge models and algorithms. In-depth information about new cognitive computing models and conceptual frameworks and their implementation. Audience The book is designed for researchers and electronics engineers, computer science engineers, industrial engineers, and mechanical engineers (both in academia and industry) working in the fields of machine learning, cognitive computing, mobile communication, and wireless network system.