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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

'Engaging, insightful, panoramic' Mustafa Suleyman, CEO of Microsoft AI &amp; Cofounder of DeepMind 'An eye-opening exploration of a revolution unfolding before our eyes' New York Journal of Books Stunning advances in digital technology have introduced a new wave of human-like AI systems. Chatbots like ChatGPT, Claude, and Gemini are already reshaping economies, challenge democracies, and reshaping society in unpredictable ways. And soon, these AI systems could make autonomous decisions on their users' behalf, transforming everything we do. Understanding how they work is crucial. Can AI systems think, know, and understand?Could they manipulate or deceive you, and if so, what might they make you do? Whose interests do they represent? When will they be able to move beyond words and take action in the real world? Neuroscientist and AI researcher Christopher Summerfield explores these questions, charting AI's evolution from early ideas in the seventeenth century to today's deep neural networks. His book is the most accessible, up-to-date, and authoritative exploration of this radical technology. With an understanding of AI's inner workings, we can address the existential question of our age: can we look forward to a technological utopia, or are we writing ourselves out of history? 'As a leading authority...Summerfield is perfectly situated to explore the meaning and implications of these machines that are so uncannily like - and unlike - ourselves' Brian Christian, co-author of Algorithms to Live by 'You might choose to be alarmed, excited, or indifferent to LLMs, but you should read Chris's book before you decide' Mike Woolridge, author of The Road to Conscious Machines

How AI Learned to Talk and What It Means

These Strange New Minds

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

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

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

Management for Professionals

This book explores how project scheduling, risk analysis and control can be understood, tested and taught through data-driven experimentation. It presents 10 Python-based example experiments that guide readers from fundamental scheduling techniques to advanced project control methods. All project data and code are provided, allowing readers to reproduce, modify, and extend every analysis.&nbsp;
The first part introduces the Critical Path Method as the foundation for structured scheduling and extends it to time–cost optimization and resource-constrained scheduling through heuristics and integer programming. The second part employs Monte Carlo simulation to capture schedule uncertainty and to measure activity sensitivity for both unconstrained and resource-limited projects. The third part focuses on project control, using Earned Value Management (EVM) to replicate forecasting accuracy studies from academic literature.&nbsp;
The book’s distinctive contribution lies in linking theoretical scheduling principles with executable Python models, enabling a transparent exploration of how data can drive project decisions. It raises questions about the adequacy and complexity of project data, the measurement of uncertainty and the balance between simplicity and realism, offering both conceptual insight and a practical laboratory for data-driven project management.The book offers an educational yet forward-looking approach, combining clear explanations with ten reproducible Python-based experiments. Readers are encouraged not only to understand, but to experiment, i.e. test and extend the models themselves. By bridging theory and practice, it provides a hands-on and reproducible framework to explore how data shapes scheduling, risk analysis, and project control. The book is particularly suited for use in courses on project management, operations research or decision analytics, as well as for self-learners eager to build technical and analytical data-driven project management skills in a structured way.

This book explores how project scheduling, risk analysis and control can be understood, tested and taught through data-driven experimentation. It presents 10 Python-based example experiments that guide readers from fundamental scheduling techniques to advanced project control methods. All project data and code are provided, allowing readers to reproduce, modify, and extend every analysis.&nbsp;The first part introduces the Critical Path Method as the foundation for structured scheduling and extends it to time–cost optimization and resource-constrained scheduling through heuristics and integer programming. The second part employs Monte Carlo simulation to capture schedule uncertainty and to measure activity sensitivity for both unconstrained and resource-limited projects. The third part focuses on project control, using Earned Value Management (EVM) to replicate forecasting accuracy studies from academic literature.&nbsp;The book’s distinctive contribution lies in linking theoretical scheduling principles with executable Python models, enabling a transparent exploration of how data can drive project decisions. It raises questions about the adequacy and complexity of project data, the measurement of uncertainty and the balance between simplicity and realism, offering both conceptual insight and a practical laboratory for data-driven project management.The book offers an educational yet forward-looking approach, combining clear explanations with ten reproducible Python-based experiments. Readers are encouraged not only to understand, but to experiment, i.e. test and extend the models themselves. By bridging theory and practice, it provides a hands-on and reproducible framework to explore how data shapes scheduling, risk analysis, and project control. The book is particularly suited for use in courses on project management, operations research or decision analytics, as well as for self-learners eager to build technical and analytical data-driven project management skills in a structured way.

Optimizing Schedules, Simulating Risk and Analyzing Project Performance through 10 Example Experiments

Data-Driven Project Management with Python

Build a more secure network with PF. The OpenBSD packet filter, PF, is central to the OpenBSD and FreeBSD network toolbox. With more services placing high demands on bandwidth and an increasingly hostile Internet environment, no sysadmin can afford to be without PF expertise. The fourth edition of The Book of PF covers the most up-to-date developments in PF, including new content on IPv6, dual stack configurations, the “queues and priorities” traffic-shaping system, NAT and redirection, wireless networking, spam fighting, failover provisioning, logging, and more.&nbsp; You’ll also learn how to: <ul><li>Create rulesets for all kinds of network traffic, IPv4 and IPv6 both, whether crossing a simple LAN, hiding behind NAT, traversing DMZs, or spanning bridges or wider networks</li><li>Set up wireless networks with access points, and lock them down using authpf and special access restrictions</li><li>Maximize flexibility and service availability via CARP, relayd, and redirection</li><li>Build adaptive firewalls to proactively defend against attackers and spammers</li><li>Harness OpenBSD’s latest traffic-shaping system to keep your network responsive, or use ALTQ and Dummynet configurations on FreeBSD to full effect</li><li>Stay in control of your traffic with monitoring and visualization tools (including NetFlow)</li></ul> The Book of PF is the essential guide to building a secure network with PF. With a little effort and this book, you’ll be well prepared to unlock PF’s full potential. Covers OpenBSD 7.x, FreeBSD 14.x, and NetBSD 10.x

Build a more secure network with PF. The OpenBSD packet filter, PF, is central to the OpenBSD and FreeBSD network toolbox. With more services placing high demands on bandwidth and an increasingly hostile Internet environment, no sysadmin can afford to be without PF expertise. The fourth edition of The Book of PF covers the most up-to-date developments in PF, including new content on IPv6, dual stack configurations, the “queues and priorities” traffic-shaping system, NAT and redirection, wireless networking, spam fighting, failover provisioning, logging, and more.&nbsp; You’ll also learn how to: <ul><li>Create rulesets for all kinds of network traffic, IPv4 and IPv6 both, whether crossing a simple LAN, hiding behind NAT, traversing DMZs, or spanning bridges or wider networks</li><li>Set up wireless networks with access points, and lock them down using authpf and special access restrictions</li><li>Maximize flexibility and service availability via CARP, relayd, and redirection</li><li>Build adaptive firewalls to proactively defend against attackers and spammers</li><li>Harness OpenBSD’s latest traffic-shaping system to keep your network responsive, or use ALTQ and Dummynet configurations on FreeBSD to full effect</li><li>Stay in control of your traffic with monitoring and visualization tools (including NetFlow)</li></ul> The Book of PF is the essential guide to building a secure network with PF. With a little effort and this book, you’ll be well prepared to unlock PF’s full potential. Covers OpenBSD 7.x, FreeBSD 14.x, and NetBSD 10.x

A No-Nonsense Guide to the OpenBSD Firewall

The Book of PF, 4th Edition

Your spellbook for code close to the metal. In hacker circles, heavy wizardry means code that trades on intimate knowledge of a system—the kind that speaks its private dialects and bends its rules. Heavy Wizardry 101 teaches you to write it. You’ll build shellcode, backdoors, droppers, and worms from scratch, working at the instruction level where magic happens. Every project is implemented for four architectures: x86_64, ARM, MIPS, and RISC-V. You’ll write the same program four times, seeing exactly what stays constant and what changes. Each chapter starts with architecture fundamentals, such as memory layout, calling conventions, and stack mechanics, then walks through complete working implementations on Linux in safe, contained environments. Learn to: <ul><li>Write position-independent shellcode and programs that execute in constrained memory spaces</li><li>Implement TCP reverse shells that bypass basic network restrictions</li><li>Build executable droppers that embed and deploy payloads without detection</li><li>Create self-replicating worms that propagate across networked systems</li><li>Write basic port scanners to discover services in a network</li><li>Port low-level code between architectures by understanding instruction-level differences</li></ul> Whether you’re a penetration tester who wants to stop depending on Metasploit, a security researcher analyzing malware, or a systems programmer who needs to understand how computers work, this book gives you the skills to build and reason about code at the lowest level.

Your spellbook for code close to the metal. In hacker circles, heavy wizardry means code that trades on intimate knowledge of a system—the kind that speaks its private dialects and bends its rules. Heavy Wizardry 101 teaches you to write it. You’ll build shellcode, backdoors, droppers, and worms from scratch, working at the instruction level where magic happens. Every project is implemented for four architectures: x86_64, ARM, MIPS, and RISC-V. You’ll write the same program four times, seeing exactly what stays constant and what changes. Each chapter starts with architecture fundamentals, such as memory layout, calling conventions, and stack mechanics, then walks through complete working implementations on Linux in safe, contained environments. Learn to: <ul><li>Write position-independent shellcode and programs that execute in constrained memory spaces</li><li>Implement TCP reverse shells that bypass basic network restrictions</li><li>Build executable droppers that embed and deploy payloads without detection</li><li>Create self-replicating worms that propagate across networked systems</li><li>Write basic port scanners to discover services in a network</li><li>Port low-level code between architectures by understanding instruction-level differences</li></ul> Whether you’re a penetration tester who wants to stop depending on Metasploit, a security researcher analyzing malware, or a systems programmer who needs to understand how computers work, this book gives you the skills to build and reason about code at the lowest level.

Shellcodes, Backdoors, Droppers and Worms

Heavy Wizardry 101

What AI Can’t Do Without You

The Human Advantage

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' Guardian As 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

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

This open access book is a unique resource in computational mathematical optimization. It conveys the entire spectrum from the basic theory to the most advanced tricks in reformulations and decomposition approaches to solving mixed-integer linear programs. The book has useful and partly previously unpublished facts for students, teachers, and practitioners alike. The content covers algorithmic ideas like column generation, cutting planes, and branch-and-price, reformulation techniques like Dantzig-Wolfe decomposition and Lagrangian relaxation, the very important application domains of vehicle routing and crew scheduling, and has some practical advice when it comes to an implementation. Many notes and even more didactic illustrations and examples bring the theory to life, complemented by almost 140 exercises, including solutions. As a side effect, readers learn ways how to formulate integer programming models for a great variety of combinatorial optimization problems. The material draws from more than 400 references and the four authors' own decades of experience in the field. Several photos taken by the authors bring a personal touch to the writing, among other tiny anecdotal elements that the connoisseur will appreciate. The book is not, and cannot be encyclopedic, but it has the ambition to be the standard text and main reference in the field for the years to come.
Students, researchers, and practitioners aiming to solve large, complex discrete and combinatorial optimization problems—whether in industry, science, or technology—will find this book essential for exploring optimal and near-optimal solutions through decomposition methods and column generation.

This open access book is a unique resource in computational mathematical optimization. It conveys the entire spectrum from the basic theory to the most advanced tricks in reformulations and decomposition approaches to solving mixed-integer linear programs. The book has useful and partly previously unpublished facts for students, teachers, and practitioners alike. The content covers algorithmic ideas like column generation, cutting planes, and branch-and-price, reformulation techniques like Dantzig-Wolfe decomposition and Lagrangian relaxation, the very important application domains of vehicle routing and crew scheduling, and has some practical advice when it comes to an implementation. Many notes and even more didactic illustrations and examples bring the theory to life, complemented by almost 140 exercises, including solutions. As a side effect, readers learn ways how to formulate integer programming models for a great variety of combinatorial optimization problems. The material draws from more than 400 references and the four authors' own decades of experience in the field. Several photos taken by the authors bring a personal touch to the writing, among other tiny anecdotal elements that the connoisseur will appreciate. The book is not, and cannot be encyclopedic, but it has the ambition to be the standard text and main reference in the field for the years to come.Students, researchers, and practitioners aiming to solve large, complex discrete and combinatorial optimization problems—whether in industry, science, or technology—will find this book essential for exploring optimal and near-optimal solutions through decomposition methods and column generation.

Branch-and-Price

Build intelligent applications—no data science degree required. Your boss is pitching new AI features. Your team is buzzing about MCP servers. Job postings are asking for AI experience with RAG, vector databases, fine-tuning, and agents. You can feel the excitement. You see the potential. You may be wondering how to get started in AI without a data science degree. You’re in the right place. The Developer’s Guide to AI gives working developers a practical path through the terminology, tools, and implementation patterns that matter. It shows you how to build with AI using the tools you already know: JavaScript, Python, APIs, SDKs, and databases. By the end of this book, you’ll know how to: <ul><li>Call LLM APIs and stream intelligent responses directly to your UI.</li><li>Engineer prompts that produce reliable, production-ready results.</li><li>Build RAG pipelines using vector databases to give AI access to your private data.</li><li>Fine-tune models with LoRA for specialized tasks like classification.</li><li>Deploy AI agents using tool-calling and the Model Context Protocol (MCP) to reason and act inside real workflows.</li></ul> LLMs, RAG, LoRA, MCP, embeddings, and agents are not just intimidating buzzwords. They are the building blocks for the next generation of software. Grab your code editor, bring your engineering instincts, and let’s build what’s next!

Build intelligent applications—no data science degree required. Your boss is pitching new AI features. Your team is buzzing about MCP servers. Job postings are asking for AI experience with RAG, vector databases, fine-tuning, and agents. You can feel the excitement. You see the potential. You may be wondering how to get started in AI without a data science degree. You’re in the right place. The Developer’s Guide to AI gives working developers a practical path through the terminology, tools, and implementation patterns that matter. It shows you how to build with AI using the tools you already know: JavaScript, Python, APIs, SDKs, and databases. By the end of this book, you’ll know how to: <ul><li>Call LLM APIs and stream intelligent responses directly to your UI.</li><li>Engineer prompts that produce reliable, production-ready results.</li><li>Build RAG pipelines using vector databases to give AI access to your private data.</li><li>Fine-tune models with LoRA for specialized tasks like classification.</li><li>Deploy AI agents using tool-calling and the Model Context Protocol (MCP) to reason and act inside real workflows.</li></ul> LLMs, RAG, LoRA, MCP, embeddings, and agents are not just intimidating buzzwords. They are the building blocks for the next generation of software. Grab your code editor, bring your engineering instincts, and let’s build what’s next!

The Developer's Guide to AI

Causal Inference with Bayesian Networks

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