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Chad Sanderson: Data Contracts

Data Contracts

Developing Production Grade Pipelines at Scale

Techniques and Applications

Deep Learning for Intrusion Detection

Learn to build a web app that can monitor millions of posts across all social media platforms to chart public opinion in real time. Social media has become our modern-day agora, the place where we get our news and engage in political discourse. With disinformation, hate speech, foreign interference, authoritarianism, market monopolization, and political polarization all on the rise, monitoring social media in the public interest has never been so difficult nor so urgent. Social Media Observatory is an in-depth beginner’s guide to coding full-stack web applications in Python to track and analyze social media activity at scale, giving you unparalleled insights into the cultural and political conversations of our time, then empowering you to reach your own conclusions. You’ll start by coding a web app to monitor Russian disinformation channels discussing Ukraine on Telegram, then learn more adversarial methods to scrape the Wayback Machine, Instagram, and TikTok. You’ll detect Twitter bots following Saudi influencers, extract text-embedded URLs to reveal a white supremacist blogosphere, parse archived tweets to reveal the discourse leading up to the January 6 insurrection, and learn to circumvent Meta's and ByteDance's defenses to scrape Instagram and TikTok, collecting evidence of war crimes in the Middle East. Finally, you’ll write an API and deploy your observatory to a cloud server to collaborate with other investigators. By the end of the book, you’ll have a working website that allows collaborators to log in, request data collection, and perform analysis of collected data, via a point-and-click interface.&nbsp; Along the way, you’ll learn to: <ul><li>Use Python and RabbitMQ to retrieve Telegram data from a remote API</li><li>Store data with PostgreSQL and query it with SQLAlchemy</li><li>Analyze the data you’ve retrieved with Pandas, NetworkX, Gephi, and other tools</li><li>Use state-of-the-art AI tools to transcribe audio and classify text</li><li>Deploy a website with an interactive dashboard using FastAPI, Dash, Cytoscape, Uvicorn, Gunicorn, Nginx, AWS, and Let’s Encrypt</li><li>Containerize your apps with Docker and provision infrastructure with Pulumi</li></ul> Whether you’re a citizen scientist, a security researcher, a journalist, an activist, or a scholar, Social Media Observatory is an essential blueprint for building your window into online worlds.

Learn to build a web app that can monitor millions of posts across all social media platforms to chart public opinion in real time. Social media has become our modern-day agora, the place where we get our news and engage in political discourse. With disinformation, hate speech, foreign interference, authoritarianism, market monopolization, and political polarization all on the rise, monitoring social media in the public interest has never been so difficult nor so urgent. Social Media Observatory is an in-depth beginner’s guide to coding full-stack web applications in Python to track and analyze social media activity at scale, giving you unparalleled insights into the cultural and political conversations of our time, then empowering you to reach your own conclusions. You’ll start by coding a web app to monitor Russian disinformation channels discussing Ukraine on Telegram, then learn more adversarial methods to scrape the Wayback Machine, Instagram, and TikTok. You’ll detect Twitter bots following Saudi influencers, extract text-embedded URLs to reveal a white supremacist blogosphere, parse archived tweets to reveal the discourse leading up to the January 6 insurrection, and learn to circumvent Meta's and ByteDance's defenses to scrape Instagram and TikTok, collecting evidence of war crimes in the Middle East. Finally, you’ll write an API and deploy your observatory to a cloud server to collaborate with other investigators. By the end of the book, you’ll have a working website that allows collaborators to log in, request data collection, and perform analysis of collected data, via a point-and-click interface.&nbsp; Along the way, you’ll learn to: <ul><li>Use Python and RabbitMQ to retrieve Telegram data from a remote API</li><li>Store data with PostgreSQL and query it with SQLAlchemy</li><li>Analyze the data you’ve retrieved with Pandas, NetworkX, Gephi, and other tools</li><li>Use state-of-the-art AI tools to transcribe audio and classify text</li><li>Deploy a website with an interactive dashboard using FastAPI, Dash, Cytoscape, Uvicorn, Gunicorn, Nginx, AWS, and Let’s Encrypt</li><li>Containerize your apps with Docker and provision infrastructure with Pulumi</li></ul> Whether you’re a citizen scientist, a security researcher, a journalist, an activist, or a scholar, Social Media Observatory is an essential blueprint for building your window into online worlds.

Track Disinformation, Spot Bots, and Find Out What's Really Happening

Social Media Exposed

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

Best Practices for Scaling and Optimizing Apache Spark

High Performance Spark

Python Automation For Dummies

An authoritative deep dive into the most recent machine learning approaches to hyperspectral remote sensing image processing In Machine Learning-Based Hyperspectral Image Processing, distinguished researcher Dr. Bing Zhang delivers an up-to-date discussion of machine learning-based approaches to hyperspectral image analysis. The author comprehensively reviews machine learning approaches to hyperspectral image denoising and super-resolution tasks, offering coverage of a variety of perspectives. Dr. Zhang also explores the most recent research on machine learning hyperspectral unmixing methods and hyperspectral image classification. He explains the algorithms used for hyperspectral image target and change detection, as well. Readers will also find: <ul> <li>A thorough introduction to the novel concept of applying advanced machine learning techniques to the analysis of hyperspectral imagery</li> <li>Comprehensive explorations of the most recent developments in this technology and its applications</li> <li>Practical discussions of how to effectively process and extract valuable insights from hyperspectral data</li> <li>Complete treatments of a variety of hyperspectral remote sensing image processing tasks, including classification, target detection, and change detection.</li> </ul> Perfect for postgraduate students and research scientists with an interest in the subject, Machine Learning-Based Hyperspectral Image Processing will also benefit researchers, academicians, and students engaged in machine learning-based approaches to image analysis.

Machine Learning-Based Hyperspectral Image Processing

An Introduction to String Algorithms

Develop the skills to stop AI-powered cyberattacks and leverage AI to improve your existing cybersecurity strategies.

Use AI to Protect Your Organization

Artificial Intelligence in Cybersecurity

You've experienced the shiny, point-and-click surface of your Linux computer—now dive below and explore its depths with the power of the command line. The Linux Command Line takes you from your very first terminal keystrokes to writing full programs in Bash, the most popular Linux shell (or command line). Along the way you'll learn the timeless skills handed down by generations of experienced, mouse-shunning gurus: file navigation, environment configuration, command chaining, pattern matching with regular expressions, and more. In addition to that practical knowledge, author William Shotts reveals the philosophy behind these tools and the rich heritage that your desktop Linux machine has inherited from Unix supercomputers of yore. As you make your way through the book's short, easily-digestible chapters, you'll learn how to: <ul><li>Create and delete files, directories, and symlinks</li><li>Administer your system, including networking, package installation, and process management</li><li>Use standard input and output, redirection, and pipelines</li><li>Edit files with Vi, the world's most popular text editor</li><li>Write shell scripts to automate common or boring tasks</li><li>Slice and dice text files with cut, paste, grep, patch, and sed</li></ul> Once you overcome your initial "shell shock," you'll find that the command line is a natural and expressive way to communicate with your computer. Just don't be surprised if your mouse starts to gather dust.

A Complete Introduction

The Linux Command Line, 3rd Edition

Tiny Machine Learning Techniques for Constrained Devices

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.

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

Data Contracts

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