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Chad Sanderson: Data Contracts

Data Contracts

Developing Production Grade Pipelines at Scale

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Sustainable Supply Chain Management: Computational Approaches and Modelling provides a comprehensive understanding of how computational approaches can be leveraged to address the challenges associated with sustainable supply chain management. It offers insights into emerging and advanced modelling techniques to optimize supply chain operations while considering environmental, social, and economic factors. This book will be a valuable resource for researchers, practitioners, and policymakers interested in understanding and implementing sustainable supply chain management practices through computational approaches and modelling. This book helps researchers understand the opportunities, challenges, and solutions by developing innovative computational tools and techniques to integrate sustainability considerations into supply chain decision-making processes.

Computational Approaches and Modelling

Sustainable Supply Chain Management

Data is critical to ensuring the success of any business. A strong data governance programme can help any organization ensure that its data is high-quality, secure and accessible.

Effective Data Governance is a step-by-step guide for mid to senior data professionals to design a simplified data governance programme that meet the needs of their organization. Covering all of the processes, roles, standards and metrics to ensure the effective and efficient use of data within an organization, data governance is essential for every business. Beginning with an explanation of the importance of data governance and its challenges, this book covers how to plan your data governance approach, build a data governance framework and write a tailored data governance policy.

This book also describes how to setup and identify the key roles and responsibilities connected to data in an organization. It shows you how to identify the right process, resolve data quality issues, manage data risk and controls and track your progress. With real-world examples on how to implement the right type of data governance programme, this actionable guide is the key to the successful use of data.

Design a Framework that Works for Your Organization

Effective Data Governance

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

Application of Python in Healthcare Technology: Medical Data Analysis and Developing ML/AI Models provides comprehensive coverage of the Python programming language from a medical-based perspective. With a strong focus on utility-based learning rather than technical-based learning of Python, this title clarifies how the application of Python in the healthcare technology industry has a meaningful impact on patient care through the development of deep learning models for early prediction of diseases and the development of Clinical Decision Support System (CDS). In 13 chapters, an extensive range of topics pertaining to the intersection of Python and medical sciences is covered. Coverage defines how to set up an environment to work with python, describes Python class, methods, and functions, explains both basic and advanced statistical analysis in Python, and provides a tool to assist in the development of deep learning models such as API, Git, Github, NumPy, Pandas, and SciPy.

Medical Data Analysis and Developing ML/AI Models

Application of Python in the Healthcare Technology

This unique compendium develops a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. It introduces the key elements of a parametric statistical model: likelihood, prior, and posterior, and show how to use them to make predictions.The book covers classical techniques such as multiple regression and the Kalman filter in a clear, accessible style that has been popular with students, but also includes detailed treatments of state-of-the-art models, highlighting tree-based methods, support vector machines and kernel methods, deep learning, and reinforcement learning. Theories are supplemented by real-world examples.This reference text is useful for undergraduate, graduate and even PhD students in quantitative finance, and also to practitioners who are facing the reality that data science and machine learning are disrupting the industry.

Statistical Machine Learning With Applications In Finance

Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program, Third Edition, is for any student, executive, manager, or data professional who needs to understand and implement a data governance program, to ensure consistent, accurate and reliable data across their organization. The problem of managing data continues to grow with issues surrounding the cost of storage, exponential growth, as well as administrative, management, and security concerns - the solution to being able to scale all of these issues up is data governance which provides better services to users and saves money. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. John Ladley guides readers though the detailed process of developing a Data Governance program, including an introduction to the concepts and principles of Data Governance, Data literacy concepts, business case, strategy, systems architecture and design, the importance of team engagement, implementation, and change management. The Third Edition includes all-new coverage embracing new data capabilities such as ethics and AI integration, as well as key regulatory changes. Data governance is now inevitable, and no longer an option. John Ladley examines the impact of society on data and the relevance for organizations to simply "do better." The Third Edition updates coverage on data governance tools to achieve delivery of necessary capabilities. This valuable resource provides foundational understanding to advanced students in Data Management and related courses, as well as comprehensive guidance for beginning professionals, managers or analysts looking to improve their processes. With the provided framework and case studies, all students and professionals in the data governance field will gain key insights into launching successful and money-saving data governance programs.

Data Contracts

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