Books

In diesem Primer erklären wir Data Mesh aus der Engineering-Perspektive.

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!

Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí

Longitudinal data are powerful but complex, requiring new concepts, data structures, and models that can feel overwhelming to learn. This cheat sheet brings together the key ideas, R commands, and modelling approaches into a single workflow, helping you understand how everything fits together and providing the building blocks for mastering longitudinal data analysis.

Data Mesh is a sociotechnical approach that enables development teams to autonomously carry out data analysis. In this primer, we explain Data Mesh from the engineering perspective.

Is There a Six-Figure Freelance Developer in You?Leaving the Rat Race with Python shows you how to nurture, grow, and harness your work-from-home coding business online --- and live the good life!It's an insider's guide to freelance developing. Start your new thriving coding business now!

Reading "Learn Python the Simple Way", you'll learn how software work.At this moment, Python is one of the fundamental programming languages for Machine Learning (ML), Scientific and Numeric Computing, Robotics and even Web Development.

Unlock NVIDIA GPU performance from Rust. This practical guide takes you from GPU architecture and memory hierarchies to writing and optimizing real CUDA Rust kernels. Explore both official Rust CUDA approaches with runnable examples and learn how to build fast, production-ready GPU code without giving up Rust’s safety and clarity.

We'll be working on a platform made up of seventy-nine recipes together. It starts off as a simple task, printing a line, but by the last chapter it covers extraction, warehousing, containers, machine learning and incident response. You can't just throw away examples in your work, and you shouldn't be doing that in your examples either. You don't need to be an Airflow expert to get started. What you're really learning here isn't a tool. It's all about making sure work is repeatable, observable and safe to rerun.

AI models do not have to be huge, slow or expensive. Distilling Intelligence explores how to build smaller models that perform at scale, then secure the APIs that serve them. From compression and distributed serving to extraction attacks, observability and incident response, this book covers what it takes to run AI reliably in the real world.

Aprende a combinar SQL, Python, estadística y Machine Learning para priorizar posibles fraudes en retail. Un enfoque práctico, técnico y aplicado, con ejemplos, visualizaciones y códigos de apoyo.

Build real machine learning applications with TensorFlow.js, from your first model to production-ready systems. Learn how to train and deploy models in the browser and Node.js, tackle real-world challenges and explore modern techniques with practical JavaScript and TypeScript examples you can run and adapt.

This practical guide explains how to calculate, interpret, and improve OEE using real-world examples, enabling engineers, supervisors, and Lean practitioners to increase productivity, reduce downtime, and achieve world-class manufacturing performance.

A from-scratch AI/ML course that treats you like an engineer, not a tourist — three modules covering language-as-numbers, the math foundations, and neural networks, each concept built in raw Python first, then PyTorch, so you always know what's really happening under the hood.

The eight volumes before this one each teach a single domain, and each is deliberatelyself-contained. The exams do not ask about domains — they ask about systems, and a system spansall of them at once. "A retrieval system is slow, expensive and occasionally wrong — which do you fix first?" needsretrieval, optimisation and operations together. Every one of the 150 questions here crosses atleast two domains, because that is the specific thing a single-domain book cannot teach you.