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Fattah Y: Causal Inference with Bayesian
Networks

Causal Inference with Bayesian Networks

Build Bayesian Networks and Causal Inference Models with R and Python

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.

Build AI Models from Scratch (No PhD Required) Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today’s most powerful AI models from scratch. No experience with deep learning required! Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory. You’ll start from the basics, and using PyTorch with real datasets, you’ll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub. You’ll build and train models to:&nbsp; <ul><li>Classify and analyze images, sequences, and time series</li><li>Generate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion models</li><li>Process natural language with recurrent neural networks and transformers</li><li>Model molecules and physical systems with graph neural networks</li><li>Improve continuously through reinforcement and active learning</li><li>Predict chaotic systems with reservoir computing</li></ul> Whether you’re an engineer, scientist, or professional developer, you’ll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you’ll move from using AI tools to creating them.

Deep Learning Crash Course

Audits are no longer random-and compliance is no longer manual. Today, artificial intelligence determines which businesses get audited, how deeply investigators dig, and whether a review escalates into penalties or quietly disappears.

AI Audit Defense &amp; Risk Reduction reveals how modern enforcement systems actually work-and how smart businesses use the same AI logic to stay protected, credible, and invisible to unnecessary scrutiny.

This book is not about fear, loopholes, or hiding income. It's about designing a business that algorithms trust.
Inside, you'll discover how AI-driven tax authorities evaluate risk, how audit scores are calculated, and how to proactively shape your financial data so your business remains statistically clean, defensible, and low-priority for investigation-year after year.
In this book, you'll learn how to:Understand how AI selects businesses for IRS audits and regulatory reviews
Identify the hidden financial patterns that silently increase audit risk
Use AI to calculate and monitor your business's audit risk score
Build a complete digital audit trail that satisfies both humans and machines
Detect compliance red flags in real time-before filings are submitted
Respond to audit inquiries strategically without escalating scrutiny
Turn past audits into intelligence that lowers future risk
Design long-term compliance systems that create "audit immunity"
Make defensible financial decisions that remain explainable years later
Transform compliance from a liability into a competitive advantage


Who this book is for:Business owners and entrepreneurs
Investors and real estate professionals
Consultants, agencies, and service firms
Online businesses and multi-entity operators
Anyone who wants to grow without triggering regulatory attention



If you want to stop worrying about audits, understand how enforcement algorithms really work, and build a business that thrives under constant observation, AI Audit Defense &amp; Risk Reduction gives you the framework, mindset, and systems to do exactly that.
In the age of AI-driven enforcement, the safest businesses aren't the smallest or quietest-they're the smartest.

How Businesses Avoid Costly IRS Investigations

AI Audit Defense & Risk Reduction

How do we live alongside machines that are sometimes smarter than us? We are entering a new era. AI systems can already outperform humans in some domains while failing spectacularly in others. They can act as tutors, collaborators, researchers, strategists, and creators...yet they can also produce falsehoods, generate convincing nonsense, and fail unpredictably. The challenge is no longer simply adopting new technology. It is learning how to think, work, and live in a world where machines are sometimes smarter than us. In Co-Existence, Ethan Mollick—a Wharton professor and author of the widely read One Useful Thing newsletter—argues that the future of AI will not be defined by replacement, but by interaction. Drawing on years of research, experimentation, and frontline experience with rapidly evolving AI systems, Mollick shows readers how to: • work productively with AI while retaining judgment and autonomy; • distinguish where human insight matters most; • avoid becoming overly dependent on systems designed to persuade and imitate; • adapt creatively as AI reshapes education, business, and everyday life; and • build a durable human advantage in a world of intelligent machines. Clear-eyed, practical, and written with warmth and humor, Co-Existence is your guide to navigating the next phase of the AI revolution.

How do we live alongside machines that are sometimes smarter than us? We are entering a new era. AI systems can already outperform humans in some domains while failing spectacularly in others. They can act as tutors, collaborators, researchers, strategists, and creators...yet they can also produce falsehoods, generate convincing nonsense, and fail unpredictably. The challenge is no longer simply adopting new technology. It is learning how to think, work, and live in a world where machines are sometimes smarter than us. In Co-Existence, Ethan Mollick—a Wharton professor and author of the widely read One Useful Thing newsletter—argues that the future of AI will not be defined by replacement, but by interaction. Drawing on years of research, experimentation, and frontline experience with rapidly evolving AI systems, Mollick shows readers how to: • work productively with AI while retaining judgment and autonomy; • distinguish where human insight matters most; • avoid becoming overly dependent on systems designed to persuade and imitate; • adapt creatively as AI reshapes education, business, and everyday life; and • build a durable human advantage in a world of intelligent machines. Clear-eyed, practical, and written with warmth and humor, Co-Existence is your guide to navigating the next phase of the AI revolution.

The Next Phase of AI

Co-Existence

From the New York Times bestselling authors of Abundance, Bold, and The Future Is Faster Than You Think comes a bold exploration of what it means to stay human in a world where technology has granted us godlike power. 'Timely and important' Eric Schmidt 'A manual for optimizing our destiny' Ray Kurzweil 'An extraordinary blueprint for hope' Mo Gawdat In 1968, Stewart Brand declared: 'We are as gods - and we might as well get good at it.' Half a century later, that prophecy has come true. We can rewrite genes, edit embryos, build artificial minds, extend life, and terraform worlds. The old miracles - omniscience, omnipresence, even resurrection - are becoming standard operating procedure. But the real question isn't whether humanity can play god. It's whether we can do it wisely. In this book, the bestselling authors of Abundance and Bold return with a sweeping exploration of our species' next great transformation. Blending hard science with vivid storytelling, Peter Diamandis and Steven Kotler chart humanity's ascent from scarcity to superabundance - and the psychological, ethical, and existential challenges that come with it. Across breakthroughs in AI, robotics, genetics, longevity, and consciousness research, they reveal a paradox at the heart of progress: as our external power expands, our inner resilience must evolve to match. Abundance without meaning leads to collapse. Intelligence without wisdom leads to extinction. To thrive in a world of everything, everywhere, all the time, we must learn to wield our godlike powers with humility, creativity, and flow. Equal parts warning and invitation, We Are As Gods is a map for flourishing in the exponential century. Because the future won't be built by those who fear what's coming, but by those who know how to turn chaos into creation. Abundance is here. Are you ready?

A Survival Guide for the Age of Abundance

We Are As Gods

From Conceptual Algorithms to Real-World Applications

Methods of Nonsmooth Optimization in Stochastic Programming

Longlisted for the Financial Times Business Book of the Year 2026'Superb' Philip Pullman 'Revelatory' George SaundersIt presented itself as a form of futurism. But the future envisioned by the architects of the Artificial State looked, chillingly, like the very worst of the past...We live in the age of data. Over the past century, rapidly evolving technologies have transformed every aspect of our society, in troubling and seemingly unstoppable ways. Political campaigns have been reduced to attention-mining algorithms, multinational corporations control public discourse, and the era of the liberal nation-state seems to be coming to an end, replaced by the rule of artificial intelligence. Today, power is maintained through mystification: machines, we are told, know what humans cannot. Yet nothing about any of this was, or is, inevitable.In this astounding account of how technology has corroded democracy around the world, prizewinning historian Jill Lepore traces the rise and fall of what she calls the 'Artificial State'. Examining everything from robot rights and Elon Musk's origins to what data centres cost wildlife, Lepore offers a history - and a perspective - wholly missing from the debate.History, Lepore demonstrates, is driven not by machines but by individuals, societies, cultures and ideas. The architects of the Artificial State had no theory of governance, nor did they have any interest in the rule of law, or any capacity for restraint. They drew their ideas not from science, but from science fiction. Yet everything destructive that they have done can be undone. By illuminating the origins of the Artificial State, Lepore reveals how we can escape it - helping us to imagine, and fight for, a different future.

The Rise and Fall of the Artificial State

Nick Clegg, formerly one of Meta's most senior executives, presents a blueprint for reforming Big Tech while preserving the essential openness of the internet'Warns of the dangers posed to a free and open global internet by an age of autocrats and a titanic power struggle over AI' GuardianAs democracies seek to curb the power of Big Tech, as authoritarian regimes like China and Russia isolate their populations from the internet, and as the AI race supercharges divisions between competing nations, the most powerful tool for bringing us together risks being dismantled.Taking us behind the scenes at Meta, and drawing on his unique experience on the world stage of both tech and politics, Nick Clegg sets out where Big Tech has gone wrong and the radical reforms that are needed if global platforms are to ensure their long-term future.But he also shows that many of the charges against them are overstated or simply untrue, and presents a blueprint for the international cooperation that is needed if we are to preserve what makes them so vital to our future: their potential to empower and improve billions of lives.Radical, reasonable, deeply felt and disarmingly honest, How To Save the Internet reminds us how much we stand to lose on our current path - and how much we might yet gain from a global, open internet.'Entertaining . . . lucid, detailed . . . there is much to agree with' Spectator'Studded throughout [with] good analyses of the political situation around technology' Observer'A gripping and timely book. Nick Clegg writes with clarity, authority and urgency' Peter Frankopan, author of The Silk Roads'A wake-up call we cannot afford to ignore' Tony Blair'A vital read' Reid Hoffman, co-founder of LinkedIn

The Threat to Global Connection in the Age of AI and Political Conflict

How to Save the Internet

A no-nonsense guide to all the essentials you’ll need to become a TypeScript wizard and build a TypeScript application from scratch. “An invaluable resource to countless developers.” —Ryan Cavanaugh, Development Lead Microsoft TypeScript Team TypeScript makes JavaScript safer and your editor smarter. Matt Pocock’s Total TypeScript courses have trained thousands of developers by putting the editor first: You learn types by watching what TypeScript tells you, not by memorizing rules. This book brings that method to print. You’ll start by setting up a proper TypeScript environment, then immediately begin writing and annotating code. Each chapter builds on the last—primitive types to unions, objects to classes, basic generics to type transformations—with exercises woven throughout as checkpoints, not afterthoughts. Later chapters tackle what takes longer to learn: deriving types from other types, writing type predicates and assertion functions, configuring the compiler for real projects, and building type architectures that don’t collapse under their own weight. You’ll learn to: <ul><li>Read TypeScript’s error messages and use your editor as a teaching tool</li><li>Write precise types for objects, functions, and classes</li><li>Use unions, narrowing, and generics to make types flexible without losing safety</li><li>Derive and transform types so you don’t repeat yourself</li><li>Configure tsconfig.json for React, Node, or library development</li></ul> This is a workshop in print. The one Matt Pocock has been refining for years.

A no-nonsense guide to all the essentials you’ll need to become a TypeScript wizard and build a TypeScript application from scratch. “An invaluable resource to countless developers.” —Ryan Cavanaugh, Development Lead Microsoft TypeScript Team TypeScript makes JavaScript safer and your editor smarter. Matt Pocock’s Total TypeScript courses have trained thousands of developers by putting the editor first: You learn types by watching what TypeScript tells you, not by memorizing rules. This book brings that method to print. You’ll start by setting up a proper TypeScript environment, then immediately begin writing and annotating code. Each chapter builds on the last—primitive types to unions, objects to classes, basic generics to type transformations—with exercises woven throughout as checkpoints, not afterthoughts. Later chapters tackle what takes longer to learn: deriving types from other types, writing type predicates and assertion functions, configuring the compiler for real projects, and building type architectures that don’t collapse under their own weight. You’ll learn to: <ul><li>Read TypeScript’s error messages and use your editor as a teaching tool</li><li>Write precise types for objects, functions, and classes</li><li>Use unions, narrowing, and generics to make types flexible without losing safety</li><li>Derive and transform types so you don’t repeat yourself</li><li>Configure tsconfig.json for React, Node, or library development</li></ul> This is a workshop in print. The one Matt Pocock has been refining for years.

Total Typescript

This book addresses the issue of algorithmic discrimination, assessing the extent to which the existing European legal framework on equality and non-discrimination can capture this phenomenon and provide effective legal protection. Intended primarily for legal scholars, it identifies potential safeguards while highlighting gaps and weaknesses in the current framework's ability to address unfair outcomes arising from algorithmic decision-making.
 
 The book examines the characteristics of algorithmic decision-making and the ways discrimination can emerge within these processes. It traces the evolution of the European Union's approach to equality and non-discrimination, from a focus on market integration to a broader emphasis on social objectives and fundamental rights. It further analyzes the challenges that algorithmic discrimination and differentiation pose to the structure and logic of EU non-discrimination law and concludes with a discussion of algorithmic discrimination in employment, considering the specific rights and interests of workers at stake.

A New Challenge for the European Union Legal Order

Algorithmic Discrimination

This open access book critically analyses a wide range of consumer law issues generated or exacerbated by the Internet of Things. The Internet of Things challenges many of the dichotomies which have shaped current laws: goods-services, hardware-software, tangible-intangible, online-offline. The book focuses on 4 specific challenges: things-as-service, liability, bricking of physical devices through software control, and the potential for enhanced personalisation, especially for digital assistants. The chapters analyse specific applications such as smart homes, connected cars, and consumer wearables with a view to providing innovative solutions to the new challenges posed by the Internet of Things, and to steer the development of the Internet of Things in a fairer direction. As well as offering an important analysis of the potential for smart consumer laws in the Internet of Things, the book also provides important insights into the growing divergence between Germany's and the UK's consumer laws since Brexit. The ebook editions of this book are available open access under a CC BY 4.0 licence on bloomsburycollections.com. Open access was funded by UKRI (Arts and Humanities Research Council) and the German Research Foundation (DFG).

Lessons from Germany and the United Kingdom

From Smart Technologies to Smart Consumer Law

Neue Schriften zum Staatsrecht

Künstliche Intelligenz darf den Richter unterstützen, ihn aber nicht ersetzen - doch wo verläuft diese Grenze konkret? Aus der Rechtsprechung des Bundesverfassungsgerichts lässt sich eine richterliche Pflicht zur eigenverantwortlichen Aufgabenwahrnehmung ableiten. Anhand zahlreicher Praxisbeispiele untersucht der Autor, wie der inhaltliche Einfluss algorithmischer und sonstiger fremder Erwägungen auf die Entscheidungsfindung mit der richterlichen Eigenverantwortung in Einklang zu bringen ist. Darauf aufbauend entwickelt die Arbeit ein Modell zur Abgrenzung zulässiger richterlicher Unterstützung von unzulässiger Ersetzung. Sie liefert damit Orientierung für Justiz, Gesetzgebung und Rechtswissenschaft in Zeiten wachsender Digitalisierung.

Zur Abgrenzung zwischen unzulässiger Ersetzung und zulässiger Unterstützung des menschlichen Richters

Richterliche Verantwortung und Künstliche Intelligenz

De Gruyter Studium

Nach einer allgemeinen Diskussion formaler Sprachen, deren Beschreibungen und Grenzfällen der Erkennbarkeit werden die regulären Sprachen behandelt, welche in der lexikalischen Defi nition von Programmiersprachen ihre wichtigste Anwendung finden sowie die kontextfreien Sprachen, mit denen man die Syntax von Programmiersprachen definiert. Aus theoretischer Sicht befriedigend ist die eindeutige Entsprechung zwischen Sprachbeschreibung und Spracherkennung - den regulären Sprachen entsprechen die endlichen Automaten und den kontextfreien Sprachen die Stackmaschinen. Weitere Stufen der Chomsky-Hierarchie werden nur kurz behandelt, da sie in der Praxis von geringerer Bedeutung sind. Stattdessen zeigt ein eigenes Kapitel zum Thema Compilerbau weitere Techniken auf, die aus einer Sprachbeschreibung einen Parser, also das komplette 'front-end' eines Compilers, entstehen lassen. Der Begriff des 'Algorithmus' wird anhand verschiedener Maschinenmodelle erklärt und bestätigt wird auch die Churchsche These, dass jede vernünftige Defi nition von 'Berechenbarkeit' auf die gleiche Klasse von Funktionen führt. Die Grenzen des algorithmisch Machbaren werden anhand des Halteproblems und des Satzes von Rice klar abgesteckt. Das abschließende Kapitel zur Komplexitätstheorie erkundet unter den lösbaren Problemen die Grenze zwischen denen, die mit einem vertretbaren (polynomiellen) Aufwand lösbar sind und solchen, deren Lösung nicht wesentlich effi zienter ist, als ein systematisches Ausprobieren von Lösungskandidaten. Dieses Kapitel führt den Leser zu dem bekanntesten noch ungelösten Problem der Theoretischen Informatik: P = NP? Der erste Band der Informatik erklärt die grundlegenden Konzepte: Programmierung, Algorithmen und Datenstrukturen. Der zweite Band ist technischen Themen gewidmet - insbesondere der Rechnerarchitektur, Betriebssystemen, Rechnernetzen und speziell dem Internet. Das Buch richtet sich an alle Einsteiger, die sich ernsthaft mit Informatik beschäftigen wollen, sei es zum Selbststudium oder zur Begleitung von Vorlesungen. In den folgenden Bänden dieses Buches werden die Themen, Rechnerarchitektur, Betriebssysteme, Rechnernetze, Internet, Compilerbau und Theoretische Informatik vertieft. Prof. Dr. Heinz-Peter Gumm ist Professor für Theoretische Informatik in Marburg. Nach dem Studium in Darmstadt und Winnipeg (Kanada) von 1970 bis 1975 und der Habilitation 1981 folgten Professuren in Hawaii, Kalifornien und New York. Seine Forschungsgebiete sind Formale Methoden, Allgemeine Algebren und Coalgebren. Prof. Dr. Manfred Sommer ist emeritierter Professor für Praktische Informatik in Marburg. Nach dem Studium in Göttingen und München von 1964 bis 1969, war er Assistent am ersten Informatik-Institut in Deutschland an der TU München. Es folgten zehn Jahre bei Siemens in München und von 1984 bis 2014 war er Informatik-Professor in Marburg.

Nach einer allgemeinen Diskussion formaler Sprachen, deren Beschreibungen und Grenzfällen der Erkennbarkeit werden die regulären Sprachen behandelt, welche in der lexikalischen Defi nition von Programmiersprachen ihre wichtigste Anwendung finden sowie die kontextfreien Sprachen, mit denen man die Syntax von Programmiersprachen definiert. Aus theoretischer Sicht befriedigend ist die eindeutige Entsprechung zwischen Sprachbeschreibung und Spracherkennung - den regulären Sprachen entsprechen die endlichen Automaten und den kontextfreien Sprachen die Stackmaschinen. Weitere Stufen der Chomsky-Hierarchie werden nur kurz behandelt, da sie in der Praxis von geringerer Bedeutung sind. Stattdessen zeigt ein eigenes Kapitel zum Thema Compilerbau weitere Techniken auf, die aus einer Sprachbeschreibung einen Parser, also das komplette »front-end« eines Compilers, entstehen lassen. Der Begriff des »Algorithmus« wird anhand verschiedener Maschinenmodelle erklärt und bestätigt wird auch die Churchsche These, dass jede vernünftige Defi nition von »Berechenbarkeit« auf die gleiche Klasse von Funktionen führt. Die Grenzen des algorithmisch Machbaren werden anhand des Halteproblems und des Satzes von Rice klar abgesteckt. Das abschließende Kapitel zur Komplexitätstheorie erkundet unter den lösbaren Problemen die Grenze zwischen denen, die mit einem vertretbaren (polynomiellen) Aufwand lösbar sind und solchen, deren Lösung nicht wesentlich effi zienter ist, als ein systematisches Ausprobieren von Lösungskandidaten. Dieses Kapitel führt den Leser zu dem bekanntesten noch ungelösten Problem der Theoretischen Informatik: P = NP? Der erste Band
 der 
 Informatik
 erklärt die grundlegenden Konzepte: Programmierung, Algorithmen und Datenstrukturen. Der 
 zweite Band
 ist technischen Themen gewidmet – insbesondere der Rechnerarchitektur, Betriebssystemen, Rechnernetzen und speziell dem Internet. 
 
 Das Buch richtet sich an alle Einsteiger, die sich ernsthaft mit Informatik beschäftigen wollen, sei es zum Selbststudium oder zur Begleitung von Vorlesungen. In den folgenden Bänden dieses Buches werden die Themen, Rechnerarchitektur, Betriebssysteme, Rechnernetze, Internet, Compilerbau und Theoretische Informatik vertieft. 
 Prof. Dr. Heinz-Peter Gummist Professor für Theoretische Informatik in Marburg. Nach dem Studium in Darmstadt und Winnipeg (Kanada) von 1970 bis 1975 und der Habilitation 1981 folgten Professuren in Hawaii, Kalifornien und New York. Seine Forschungsgebiete sind Formale Methoden, Allgemeine Algebren und Coalgebren. Prof. Dr. Manfred Sommerist emeritierter Professor für Praktische Informatik in Marburg. Nach dem Studium in Göttingen und München von 1964 bis 1969, war er Assistent am ersten Informatik-Institut in Deutschland an der TU München. Es folgten zehn Jahre bei Siemens in München und von 1984 bis 2014 war er Informatik-Professor in Marburg.

Formale Sprachen, Compilerbau, Berechenbarkeit und Komplexität

Informatik

Causal Inference with Bayesian Networks

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