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Catalogue de droit

Konieczny B: Data Engineering Design
Patterns

Data Engineering Design Patterns

Recipes for Solving the Most Common Data Engineering Problems

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

Analytics and AI for Healthcare

The book provides an authoritative and accessible guide on integrating intelligent technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), into modern eye care.Written by experts in ophthalmology, medical AI, and healthcare innovation, it offers a multidisciplinary exploration of how these tools are fundamentally redefining ophthalmic diagnostics, therapeutics, and surgical interventions. The core focus is on bridging the gap between technical AI innovations and their clinical deployment, offering a practical roadmap for clinicians and researchers. It combines cutting-edge research with real-world applications to demonstrate how AI is being used for the early and accurate detection of retinal diseases and glaucoma, enhancing surgical precision via robotics, and guiding personalized treatment plans. Three key areas are studied throughout the book. First, AI-powered imaging diagnostics, to explore how ML and DL models are used for the early and accurate detection of retinal diseases and glaucoma, supporting faster and more precise decision-making. Second, the application of AI-assisted surgical robotics in ophthalmology: In this topic, we shall examine how AI integration in robotic surgery enhances precision, reduces risk, and supports surgical planning. Third, ethical and regulatory considerations to address the responsible adoption of AI, including data governance, patient privacy, and health equity in ophthalmic care. These topics are essential for stakeholders navigating the clinical, technical, and ethical dimensions of AI implementation and adoption. Ultimately, the text aims to show how the responsible adoption of AI is actively improving clinical outcomes and reshaping the future of vision science.The primary audience for this book includes students taking advanced undergraduate or postgraduate courses on medical technology, medical imaging and healthcare bioinformatics. It would also serve as a useful guide for practicing ophthalmologists, optometrists, biomedical engineers, computer vision experts, and AI developers, working in the healthcare and medical related areas.

Revolutionizing Eye Care with Machine Learning, and Deep Learning

AI in Modern Ophthalmology

Red Rock Publishers

The gap between AI's promise and its performance isn't a technology problem. It's a human one.

Most AI projects don't fail loudly. They fail quietly - after months of work, when nothing actually changes.

It doesn't matter whether you run a five-person business or a global enterprise. The question is the same: are you solving a real problem, or chasing a shiny solution?

Our own team once built a chatbot that answered business questions in plain English. In testing, it was magical - 85% accuracy and applause all around. Then we rolled it out to real users, and accuracy fell below 45%. Real people asked entirely different questions than the ones we'd trained for. Months of work. Zero adoption.

Klarna, Duolingo, and IBM all made versions of the same mistake - at far greater scale. The problem was never the technology. It was the question nobody asked before building it.

The AI Decision Map is written for the person who has to make AI work - not the engineer who builds it, but the leader who must answer for it. No jargon. No code.

Drawing on two decades of technology leadership across three continents, doctoral research into why people resist - or embrace - new technology, and candid conversations with hundreds of executives on his podcast, Vin Mitty delivers hard-won clarity about what works, what fails, and why.
Two frameworks. One system.The AI Decision Map(TM) - decide where to apply intelligence, what problem to solve, and how to measure risk, before you build anything.
The AI Value Review(TM) - measure whether it's working, learn from outcomes, and sustain performance long after launch.



This book is for you if you have ever sat in a meeting where someone said "We should be doing more with AI" - and felt a quiet dread.
For the small business owner wondering whether to invest in AI - or wait
For the manager who needs to lead a technology initiative without a technical background
For the executive being pressured by a board that has been reading too many headlines
For anyone accountable for AI outcomes who was never handed a playbook



Readers who have enjoyed Thinking, Fast and Slow, The Innovator's Dilemma, or Good to Great will find this book speaks the same language: clear thinking, human insight, and ideas that genuinely change how you see a problem.

Built from two decades of practice, doctoral research on AI adoption, and real conversations with leaders across healthcare, financial services, legal, education, manufacturing, and retail - this is the decision-making guide executives and owners actually need.

Excitement isn't a strategy. Frameworks turn energy into real value.

The AI Decision Map

As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents. Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve. <ul> <li>Learn core RAG components including embedding, retrieval, and generation techniques </li><li>Understand advanced workflows like semantic-aware chunking and multi-query prompting </li><li>Build custom solutions such as chatbots and autonomous agents for specific data challenges </li><li>Continuously evaluate and optimize systems for accuracy, relevance, and performance
</li></ul>

Practical Recipes from Data Preprocessing to LLM Agents

Rag with Python Cookbook

Transforming Risk: AI Applications in Modern Insurance explores how artificial intelligence is revolutionizing the insurance industry. The book examines AI-driven innovations in risk assessment, underwriting, fraud detection, and claims management. By integrating big data analytics, machine learning, and predictive modeling, insurers can make faster, more accurate decisions while enhancing customer experience. It also addresses ethical considerations, regulatory challenges, and the future role of human expertise in an AI-powered landscape. Case studies illustrate real-world applications, from personalized policy pricing to proactive risk prevention. This insightful guide is essential for insurance professionals, technology leaders, and policymakers navigating the evolving digital insurance ecosystem.

AI Applications in Modern Insurance

Transforming Risk

New Machine Learning Methods for the Sustainable Development Goals

Companies innovating with generative AI understand that having the right data foundation is critical for success and profitability. To best position themselves for long-term success, organizations must prioritize investments in data and AI governance. AI-Ready Data Blueprints is your map to connecting data strategy, GenAI, and ethical practices to build and scale truly effective solutions. Taking a comprehensive, cloud-agnostic approach focused on real-world business challenges, seasoned data and AI experts Navnit Shukla, Kien Pham, Srikanth Sopirala, and Harsha Tadiparthi share actionable insights to guide you in designing and implementing effective data-centric GenAI systems. Whether you're new to GenAI or are already focusing on optimizing it for accuracy, speed, or both, the principles shared in this book will empower you to excel in all your AI endeavors. <ul><li>Identify the key elements of a solid data foundation for generative AI</li> <li>Apply data governance and orchestration techniques to ensure high data quality, access control, and proper data lineage for reliable AI systems</li> <li>Optimize GenAI applications through prompt engineering, fine-tuning, and retrieval-augmented generation</li> <li>Implement security, compliance, and governance measures, including responsible AI practices, transparency, and more</li></ul>

From Raw Data to AI-Driven Innovation

AI-Ready Data Blueprints

Transform raw data into business insights Businesses run on data and Microsoft® Power BI is the engine for turning data into insight and decisions. This beginner-friendly guide helps you get started with the Power BI basics. It's also great for power users, covering advanced topics like integrating AI and programming your own functions. This updated edition shows you how to get even more power out of the tool, with enhanced collaboration, customization, and version control. Harness the power of Power BI, the easy way. Inside... <ul> <li>Turn data into business growth</li> <li>Set up Power BI in any business</li> <li>Discover trends in your data</li> <li>Collaborate in real-time</li> <li>Build interactive dashboards</li> <li>Create branded visualizations</li> <li>Use AI for enhanced efficiency</li> <li>Program your own functions</li> </ul>

Microsoft Power BI For Dummies

300 Generative AI Interview Questions and Answers

AI Engineering Interviews

A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems. <ul><li>Integrates a rich array of machine learning methods into causal modeling frameworks</li><li>Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis</li><li>Features a breadth of real-world examples from recent papers</li><li>Includes coding implementation in Python, R, and Stata</li></ul>

Applied Microeconometrics

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The third edition of this popular guide expands its practical foundations in R and Python into the modern AI toolkit, with new chapters on neural networks, deep learning, and large language models. Generative AI is integrated throughout, showing how tools such as ChatGPT, Claude, and Gemini work, and how they can support real-world statistical workflows. This book highlights concepts that matter most when working with data, building predictive models, and deploying AI responsibly. If you're comfortable with R or Python and have had some exposure to basic statistics, this concise reference will boost your statistical literacy, your understanding of how AI works, and your confidence in real-world data science and AI projects. <ul><li>Conduct exploratory analysis of data to improve quality and model outcomes</li> <li>Apply sampling and experimental design to reduce bias and answer questions with clarity</li> <li>Use regression to understand data-generating processes and detect anomalies</li> <li>Build predictive models using classification, clustering, and unsupervised learning with unbalanced data</li> </ul>

50+ Essential Concepts Using R and Python

AI-Assisted Statistics for Data Scientists

The vast potential of AI technology remains untapped in areas like audio, video, and complex data analysis. In fact, many of today's professionals find it challenging to apply AI innovations across these diverse domains due to a lack of guidance and practical implementations.This comprehensive guide, tailored especially for intermediate to advanced ML engineers, data scientists, and researchers, fills the gap. Author Nicole Koenigstein guides readers through the versatile applications of transformer models, not only deepening theoretical understanding but also emphasizing actionable strategies for real-world applications. The book equips you with a grand unified theory for transformers--foundational insights that keep you on the cutting edge regardless of state-of-the-art model evolutions. You'll discover how to apply: <ul><li>Transformers in nontext domains like image, video, and music generation</li> <li>Reasoning models, coding agents, and multi-agent architectures</li> <li>Training-time and test-time optimization strategies</li> <li>Production deployment, runtime engineering, and hardware efficiency</li></ul>

Applications Beyond Nlp

Transformers: The Definitive Guide

Data Engineering Design Patterns

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