Montbleau.ca Academic Press
The Textbook, Reborn for the Pace of Tech.
Being Published
AI Foundation
AI Foundations by Pierre Montbleau is a comprehensive, beginner-friendly yet robust guide to the fundamental concepts, theories, and technologies that drive modern artificial intelligence. The book explores AI through five major pillars—mathematics, computer science, machine learning, logic & reasoning, and cognitive science & neuroscience—showing how each discipline contributes to building intelligent systems.
It introduces readers to essential mathematical tools like linear algebra, probability, calculus, information theory, and optimization, explaining how they support neural networks, deep learning, and decision-making systems. The computer science section covers algorithms, data structures, computational complexity, and optimization strategies crucial for scalable AI systems.
The book dedicates substantial focus to machine learning, walking through supervised, unsupervised, and reinforcement learning, core algorithms, model training, evaluation, and real-world applications. It also examines symbolic AI, knowledge representation, automated reasoning, and probabilistic models, highlighting how logic underpins explainable and structured AI methods.
From a biological perspective, the book draws inspiration from cognitive science and neuroscience to explain the origins of neural networks, reinforcement learning, perception, and decision-making in machines. A full chapter is devoted to AI ethics, addressing fairness, bias, transparency, alignment, accountability, and the philosophical debates around consciousness and AGI.
Throughout, Montbleau connects theory to practice with examples from healthcare, finance, robotics, NLP, autonomous systems, climate modeling, and other domains. Each chapter concludes with summaries, exercises, and practice tests to reinforce comprehension.
In essence, AI Foundations serves as an accessible yet thorough roadmap for students, professionals, and curious readers who want to understand how AI works, why it matters, and how to apply or evaluate it responsibly in the real world.
It introduces readers to essential mathematical tools like linear algebra, probability, calculus, information theory, and optimization, explaining how they support neural networks, deep learning, and decision-making systems. The computer science section covers algorithms, data structures, computational complexity, and optimization strategies crucial for scalable AI systems.
The book dedicates substantial focus to machine learning, walking through supervised, unsupervised, and reinforcement learning, core algorithms, model training, evaluation, and real-world applications. It also examines symbolic AI, knowledge representation, automated reasoning, and probabilistic models, highlighting how logic underpins explainable and structured AI methods.
From a biological perspective, the book draws inspiration from cognitive science and neuroscience to explain the origins of neural networks, reinforcement learning, perception, and decision-making in machines. A full chapter is devoted to AI ethics, addressing fairness, bias, transparency, alignment, accountability, and the philosophical debates around consciousness and AGI.
Throughout, Montbleau connects theory to practice with examples from healthcare, finance, robotics, NLP, autonomous systems, climate modeling, and other domains. Each chapter concludes with summaries, exercises, and practice tests to reinforce comprehension.
In essence, AI Foundations serves as an accessible yet thorough roadmap for students, professionals, and curious readers who want to understand how AI works, why it matters, and how to apply or evaluate it responsibly in the real world.
Writing
Working with AI Data
Working with AI Data is a practical guide to understanding, preparing, and managing data for artificial intelligence systems. The book explains the full workflow required to turn raw information into high-quality datasets suitable for machine learning, emphasizing the importance of data quality, structure, and governance.
It introduces the fundamentals of data types—including structured, unstructured, and semi-structured data—and explains how these forms impact AI development. The book guides readers through essential data-handling processes such as data collection, cleaning, labeling, storage, and transformation. It also covers key technical concepts like databases, data pipelines, metadata, ontologies, and data warehousing.
A major focus is placed on data preparation, highlighting best practices for handling noise, missing values, duplicates, outliers, bias risks, and ethical considerations related to privacy and security. The book also explores how modern AI systems rely on well-curated training data, showing common pitfalls when data is incomplete, unbalanced, or poorly documented.
Practical examples and visual diagrams illustrate how data flows through AI systems and how teams can collaborate effectively across roles like data engineers, analysts, and machine-learning specialists. The final sections provide guidance on responsible data use, governance frameworks, and maintaining data quality over time.
Overall, Working with AI Data serves as an accessible, hands-on introduction for students, professionals, and organizations aiming to build reliable AI solutions by mastering the foundation of all AI technologies—the data itself.
It introduces the fundamentals of data types—including structured, unstructured, and semi-structured data—and explains how these forms impact AI development. The book guides readers through essential data-handling processes such as data collection, cleaning, labeling, storage, and transformation. It also covers key technical concepts like databases, data pipelines, metadata, ontologies, and data warehousing.
A major focus is placed on data preparation, highlighting best practices for handling noise, missing values, duplicates, outliers, bias risks, and ethical considerations related to privacy and security. The book also explores how modern AI systems rely on well-curated training data, showing common pitfalls when data is incomplete, unbalanced, or poorly documented.
Practical examples and visual diagrams illustrate how data flows through AI systems and how teams can collaborate effectively across roles like data engineers, analysts, and machine-learning specialists. The final sections provide guidance on responsible data use, governance frameworks, and maintaining data quality over time.
Overall, Working with AI Data serves as an accessible, hands-on introduction for students, professionals, and organizations aiming to build reliable AI solutions by mastering the foundation of all AI technologies—the data itself.
Fiction
Shadow in the wire
Shadow in the Wire is a gripping cyber-thriller that follows Ethan Cole, a former hacker dragged back into the world he tried to escape when a devastating cyberattack cripples a global bank. The attack bears the signature of Phantom Cell, a ruthless hacker collective with ambitions far beyond theft—they aim to dismantle the global financial system.
Teaming up with Lena Reyes, a rogue FBI agent with her own shadowy past, and Cipher, a brilliant young hacker driven by a personal vendetta, Ethan races to uncover Phantom Cell’s true motive. As the attacks escalate—from power grids to hospitals—Ethan discovers the mastermind is Orpheus, a legendary hacker from his past he thought was dead. The final revelation shakes him to his core: Orpheus is his ex-fiancée, Anna, who faked her death and now seeks to burn the system to the ground.
In a high-stakes climax, Ethan must confront Anna in a battle that costs lives and sacrifices, ending with her fall but leaving the door open for a new, even more dangerous threat. The novel blends cutting-edge cyber realism with themes of redemption, betrayal, and the haunting weight of the past, setting the stage for an ongoing war in the shadows.
Teaming up with Lena Reyes, a rogue FBI agent with her own shadowy past, and Cipher, a brilliant young hacker driven by a personal vendetta, Ethan races to uncover Phantom Cell’s true motive. As the attacks escalate—from power grids to hospitals—Ethan discovers the mastermind is Orpheus, a legendary hacker from his past he thought was dead. The final revelation shakes him to his core: Orpheus is his ex-fiancée, Anna, who faked her death and now seeks to burn the system to the ground.
In a high-stakes climax, Ethan must confront Anna in a battle that costs lives and sacrifices, ending with her fall but leaving the door open for a new, even more dangerous threat. The novel blends cutting-edge cyber realism with themes of redemption, betrayal, and the haunting weight of the past, setting the stage for an ongoing war in the shadows.