Practical Synthetic Data Generation Balancing Privacy And The Broad Availability Of Data Pdf
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Practical Synthetic Data Generation
Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure
Practical Simulations for Machine Learning
Author: Paris Buttfield-Addison
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2022-06-07
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits
The Ethics of Personal Data Collection in International Relations
This volume’s relevance may be explained, first and foremost, during a time of unprecedented loss of life around the world each day. The data, which is oftentimes incomplete and misleading, nonetheless reveals the state as deficient as well as negligent in its response to social healthcare needs. This volume attests to the fact that pressing global public health concerns are ever present as subjects of societal discourse and debate in developed and developing states. Moreover, the COVID-19 pandemic makes the omission of the ethics of personal data collection analysis in the international relations literature even more salient given the rise of contact tracing and increased uses of mobile phone Apps to track citizens by states and firms across the globe, as this volume’s chapters analyzing the responses to COVID-19 in Iran and Taiwan explain.