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Alferink B: Programmierung mit SCL und
dem TIA Portal

Programmierung mit SCL und dem TIA Portal

Einführung in die SPS-Programmierung mit S7-1500 und S7-1200

mitp Praxis

Python programmieren lernen und Schritt für Schritt eigene Retro-Games entwickeln

<ul><li>Für Programmieranfänger und Python-Einsteiger, die anhand konkreter Spieleprojekte direkt in die Praxis einsteigen möchten</li>
<li>8 Retro-Games mit steigendem Schwierigkeitsgrad selbst programmieren, ausprobieren, verändern und um eigene Ideen erweitern</li>
<li>Spieleentwicklung mit Python und pygame lernen: Spielfiguren steuern, Kollisionen erkennen, Highscores, Labyrinthe und Punktesysteme umsetzen</li>
<li>Moderne KI-Techniken praktisch anwenden und intelligente NPCs sowie Gegner entwickeln, die reagieren oder selbstständig dazulernen</li>
<li>Spielerischer Einstieg in Q-Learning, Mini-Max und neuronale Netze und damit zugleich in grundlegende Verfahren der künstlichen Intelligenz</li></ul>

<p< p="">
Ohne Vorkenntnisse
In diesem Buch lernst du Schritt für Schritt, wie du deine eigenen Spiele auf Basis von bekannten Spiele-Klassikern mit Python programmierst - unabhängig davon, ob du zum ersten Mal programmierst oder schon Vorkenntnisse mit Python oder einer anderen Programmiersprache hast. 
Mit Python und der Spielebibliothek pygame entwickelst du acht verschiedene Retro-Spiele, die nach und nach anspruchsvoller werden. Von einfachen Arcade-Games bis hin zu komplexen Taktik-Klassikern wird die Programmierung aller Spiele leicht verständlich erklärt, so dass du sie direkt ausprobieren, verändern und erweitern kannst.

Einsatz von KI
Mit modernen KI-Techniken bringst du deine Spiele-Klassiker auf das nächste Level, indem du Gegner entwickelst, die intelligent auf dein Verhalten reagieren oder selbstständig dazulernen.

Du lernst unter anderem:
<ul><li>Spiele mit Python und pygame entwickeln</li>
<li>Spielfiguren, Kollisionen und Highscores umsetzen</li>
<li>Labyrinthe, Punktesysteme und Spielsteuerungen programmieren</li>
<li>NPCs mit neuronalen Netzen trainieren</li>
<li>KI-Verfahren wie Q-Learning und Mini-Max praktisch anwenden</li>
<li>eigene Spielideen weiterentwickeln und anpassen</li></ul>
Damit erhältst du nicht nur einen Einstieg in die Programmierung und Spieleentwicklung mit Python, sondern auch - ganz praxisnah - in die Welt der künstlichen Intelligenz.

Spiele im Buch - basierend auf beliebten Klassikern:
<ul><li>Schlangenbiss</li>
<li>Press under Water</li>
<li>Pyramide</li>
<li>Tetrimania</li>
<li>Diamond Pusher</li>
<li>Arcade Fighter mit KI-gesteuerten Gegnern</li>
<li>Robot Chase mit KI-basierter Wegsuche</li>
<li>Landgewinn mit KI-Gegner in verschiedenen Schwierigkeitsstufen</li></ul>
</p<>

Python programmieren lernen und Schritt für Schritt eigene Retro-Games entwickeln

Für Programmieranfänger und Python-Einsteiger, die anhand konkreter Spieleprojekte direkt in die Praxis einsteigen möchten
8 Retro-Games mit steigendem Schwierigkeitsgrad selbst programmieren, ausprobieren, verändern und um eigene Ideen erweitern
Spieleentwicklung mit Python und pygame lernen: Spielfiguren steuern, Kollisionen erkennen, Highscores, Labyrinthe und Punktesysteme umsetzen
Moderne KI-Techniken praktisch anwenden und intelligente NPCs sowie Gegner entwickeln, die reagieren oder selbstständig dazulernen
Spielerischer Einstieg in Q-Learning, Mini-Max und neuronale Netze und damit zugleich in grundlegende Verfahren der künstlichen Intelligenz


Ohne Vorkenntnisse
In diesem Buch lernst du Schritt für Schritt, wie du deine eigenen Spiele auf Basis von bekannten Spiele-Klassikern mit Python programmierst – unabhängig davon, ob du zum ersten Mal programmierst oder schon Vorkenntnisse mit Python oder einer anderen Programmiersprache hast. 
Mit Python und der Spielebibliothek pygame entwickelst du acht verschiedene Retro-Spiele, die nach und nach anspruchsvoller werden. Von einfachen Arcade-Games bis hin zu komplexen Taktik-Klassikern wird die Programmierung aller Spiele leicht verständlich erklärt, so dass du sie direkt ausprobieren, verändern und erweitern kannst.

Einsatz von KI
Mit modernen KI-Techniken bringst du deine Spiele-Klassiker auf das nächste Level, indem du Gegner entwickelst, die intelligent auf dein Verhalten reagieren oder selbstständig dazulernen.

Du lernst unter anderem:
Spiele mit Python und pygame entwickeln
Spielfiguren, Kollisionen und Highscores umsetzen
Labyrinthe, Punktesysteme und Spielsteuerungen programmieren
NPCs mit neuronalen Netzen trainieren
KI-Verfahren wie Q-Learning und Mini-Max praktisch anwenden
eigene Spielideen weiterentwickeln und anpassen
Damit erhältst du nicht nur einen Einstieg in die Programmierung und Spieleentwicklung mit Python, sondern auch – ganz praxisnah – in die Welt der künstlichen Intelligenz.

Spiele im Buch - basierend auf beliebten Klassikern:
Schlangenbiss
Press under Water
Pyramide
Tetrimania
Diamond Pusher
Arcade Fighter mit KI-gesteuerten Gegnern
Robot Chase mit KI-basierter Wegsuche
Landgewinn mit KI-Gegner in verschiedenen Schwierigkeitsstufen

Spiele-Klassiker programmieren - inkl. KI-Funktionen

Retro-Games mit Python für Einsteiger

Roue und Chrome haben ein neues Ziel: Heaven Land. Auf der Suche nach Anhaltspunkten erhalten sie den Tipp, "Valhalla" anzusteuern - eine Arena, in der pausenlos Kampf-Gear aufeinandergehetzt wird. Roue ist nicht ganz wohl bei dem Gedanken, einen solch gefährlichen Ort aufzusuchen. Außerdem hat mittlerweile auch jemand ein Auge auf sie geworfen: Wraith, eine Androidin von kleiner Gestalt, doch mit viel Macht...

Roue und Chrome haben ein neues Ziel: Heaven Land. Auf der Suche nach Anhaltspunkten erhalten sie den Tipp, „Valhalla“ anzusteuern – eine Arena, in der pausenlos Kampf-Gear aufeinandergehetzt wird. Roue ist nicht ganz wohl bei dem Gedanken, einen solch gefährlichen Ort aufzusuchen. Außerdem hat mittlerweile auch jemand ein Auge auf sie geworfen: Wraith, eine Androidin von kleiner Gestalt, doch mit viel Macht…

Heart Gear – Band 2

In a world flooded with digital information, the ability to automatically extract meaningful and actionable insights from global news feeds is a critical skill. Extracting Actionable Information from RSS Feeds Using Python and AI offers a hands-on guide for leveraging Python and OpenAI to transform raw RSS content—both in English and non-English languages—into structured, insightful data.

This book walks readers through building intelligent pipelines that go beyond simple feed parsing. Using advanced natural language processing and AI techniques, readers will learn how to extract vital elements from each news article, including:

Author identification

Detailed, AI-generated summaries

Assessment of global, political, and social relevance

Detection of potential threats or risks

Named entity recognition (people, places, organizations)




Whether you're building real-time threat intelligence systems, media monitoring dashboards, or conducting geopolitical analysis, this book equips you with the tools and source code to accelerate your development. Each chapter includes fully functional Python scripts that can be immediately applied or extended to meet specific needs.

Designed for developers, analysts, and technologists, this practical and forward-looking book bridges the gap between unstructured content and actionable intelligence—at the speed of the global news cycle.

What You’ll Learn:

Understand how to collect and process RSS feed data from both English and non-English sources using Python.

Apply OpenAI-powered natural language processing to extract key elements such as author, summary, relevance, and threat indicators from news articles.

Perform named entity recognition (NER) to identify and extract people, places, and organizations mentioned in each article.

Evaluate the geopolitical, social, and political relevance of news stories using AI-driven content analysis techniques.

Utilize and customize the provided Python source code to build or enhance real-time content extraction and analysis tools.




Who This Book Is for:

Primary Target Readers include:

Data Analysts and Intelligence Professionals: Professionals in government, cybersecurity, media monitoring, or corporate intelligence who need to extract and act on relevant news information in real time.

Python Developers and AI Enthusiasts: Intermediate to advanced Python programmers looking to integrate AI for real-world content analysis, especially those interested in OpenAI and natural language processing.

Journalists and Media Researchers: Those seeking to automate content curation, perform author attribution, or assess bias and relevance across diverse news sources globally.

Academics and Students in Data Science, AI, or Digital Humanities: Educators and learners looking for applied projects in NLP, multilingual processing, and AI-driven analysis.

Tech-Savvy Policy Makers and Think Tank Researchers: Readers who monitor emerging global narratives and want automated tools to help assess political, social, and security implications.

In a world flooded with digital information, the ability to automatically extract meaningful and actionable insights from global news feeds is a critical skill. Extracting Actionable Information from RSS Feeds Using Python and AI offers a hands-on guide for leveraging Python and OpenAI to transform raw RSS content—both in English and non-English languages—into structured, insightful data.This book walks readers through building intelligent pipelines that go beyond simple feed parsing. Using advanced natural language processing and AI techniques, readers will learn how to extract vital elements from each news article, including:<ul><li>Author identification</li><li>Detailed, AI-generated summaries</li><li>Assessment of global, political, and social relevance</li><li>Detection of potential threats or risks</li><li>Named entity recognition (people, places, organizations)</li></ul>Whether you're building real-time threat intelligence systems, media monitoring dashboards, or conducting geopolitical analysis, this book equips you with the tools and source code to accelerate your development. Each chapter includes fully functional Python scripts that can be immediately applied or extended to meet specific needs.Designed for developers, analysts, and technologists, this practical and forward-looking book bridges the gap between unstructured content and actionable intelligence—at the speed of the global news cycle.What You’ll Learn:<ul><li>Understand how to collect and process RSS feed data from both English and non-English sources using Python.</li><li>Apply OpenAI-powered natural language processing to extract key elements such as author, summary, relevance, and threat indicators from news articles.</li><li>Perform named entity recognition (NER) to identify and extract people, places, and organizations mentioned in each article.</li><li>Evaluate the geopolitical, social, and political relevance of news stories using AI-driven content analysis techniques.</li><li>Utilize and customize the provided Python source code to build or enhance real-time content extraction and analysis tools.</li></ul>Who This Book Is for:Primary Target Readers include:<ul><li>Data Analysts and Intelligence Professionals: Professionals in government, cybersecurity, media monitoring, or corporate intelligence who need to extract and act on relevant news information in real time.</li><li>Python Developers and AI Enthusiasts: Intermediate to advanced Python programmers looking to integrate AI for real-world content analysis, especially those interested in OpenAI and natural language processing.</li><li>Journalists and Media Researchers: Those seeking to automate content curation, perform author attribution, or assess bias and relevance across diverse news sources globally.</li><li>Academics and Students in Data Science, AI, or Digital Humanities: Educators and learners looking for applied projects in NLP, multilingual processing, and AI-driven analysis.</li><li>Tech-Savvy Policy Makers and Think Tank Researchers: Readers who monitor emerging global narratives and want automated tools to help assess political, social, and security implications.</li></ul>

From Global Headlines to Actionable Intelligence

Extracting Intelligence from RSS News Feeds Using Python and AI

Die Digitalisierung hinterlässt ihre Spuren im Recht, etwa in der Vertragsgestaltung, bei Fragen der Strafzumessung oder im Verwaltungshandeln und ist damit Gegenstand der juristischen Ausbildung. Ausgehend von einer begrifflichen Klärung erörtert das Lehrbuch anhand der drei großen Hauptrechtsgebiete (Öffentliches Recht, Zivilrecht und Strafrecht) die Schnittstellen, an denen Recht und Digitalisierung sich treffen. 
Die 2. Auflage des rasch rezipierten Lehrbuchs widmet sich u.a.
<li>der Geltung der KI-Verordnung, mit Auswirkungen auch für das Privatrecht, 
</li><li>der Herausbildung internationaler Regeln zu Verträgen zwischen und mit KI (sog. UNCITRAL-Regelwerk), 
</li><li>den Plänen der Bundesregierung, Regeln zu Smart Contracts zu erlassen (laut Koalitionsvertrag).
</li>

Die Digitalisierung hinterlässt ihre Spuren im Recht, etwa in der Vertragsgestaltung, bei Fragen der Strafzumessung oder im Verwaltungshandeln und ist damit Gegenstand der juristischen Ausbildung. Ausgehend von einer begrifflichen Klärung erörtert das Lehrbuch anhand der drei großen Hauptrechtsgebiete (Öffentliches Recht, Zivilrecht und Strafrecht) die Schnittstellen, an denen Recht und Digitalisierung sich treffen. 
Die 2. Auflage des rasch rezipierten Lehrbuchs widmet sich u.a.
der Geltung der KI-Verordnung, mit Auswirkungen auch für das Privatrecht, 
der Herausbildung internationaler Regeln zu Verträgen zwischen und mit KI (sog. UNCITRAL-Regelwerk), 
den Plänen der Bundesregierung, Regeln zu Smart Contracts zu erlassen (laut Koalitionsvertrag).

Recht der Digitalisierung

- Numerical computing with NumPy arrays, dtypes, vectorized operations
- Data analysis using Pandas DataFrames, grouping, pivoting, and time series
- Scientific visualization with Matplotlib plots, layouts, and contour graphics
- Real-world data work: files, missing data, binning, and indexing
- Applied Python: image processing, probability, and practical projects

This book teaches the Python fundamentals required to solve numerical problems in data science and machine learning. 
The first part focuses on NumPy as the foundation of numerical programming, covering arrays as the core data type, numerical operations, broadcasting, and universal functions, as well as statistics, probability, Boolean masking, and file handling.
The second part is devoted to data visualization with Matplotlib, ranging from core concepts to line, bar, histogram, and contour plots. The third part introduces Pandas, including Series and DataFrames, importing and exporting Excel, CSV, and JSON files, handling missing data, and visualization directly within Pandas.
The fourth part presents practical applications, including a household budget project, an incomeexpenditure analysis, and an introduction to image processing.
The book concludes with a fifth part containing solutions to the numerous exercises that accompany almost every one of the 33 chapters.

WHAT'S INSIDE //
Numerical operations on multidimensional arrays/Broadcasting and universal functions (ufuncs)/Discrete &amp; continuous plots/Bar charts, histograms, and contour plots/Series and DataFrames/Working with Excel, CSV, and JSON files/Handling missing data (NaN)/Data visualization techniques/Image processing funda mentals/Budget tracking and incomeexpenditure analysis

Python Data Analysis with NumPy, Pandas, and Matplotlib

Numeric Python

Get the eBook free when you register your print book at Manning. You just built something amazing in Python and you’re ready to share it with the world! But does shipping it to the web mean…learning JavaScript? With the Streamlit framework, you can build interactive web apps entirely in Python incredibly fast. By providing a collection of pre-built UI components and streamlined configurations, Streamlit turns your ideas for data tools and AI workflows into usable applications without any tedious HTML, CSS, and JavaScript. Build Python Web Apps with Streamlit follows a proven learn-by-building approach. Each chapter introduces a new hands-on project. You’ll create data dashboards, interactive checklists, and even an AI chatbot optimized with RAG and agentic patterns. You’ll also learn from intentional mistakes and real-world debugging challenges that teach you how Streamlit actually works. As you go, each project helps you master software engineering skills you might not have learned as a Python programmer—gathering requirements, persistence and database integration, user authentication, deployment, and troubleshooting. • Build interactive web apps without HTML/CSS/JavaScript • Understand Streamlit’s execution model • Work with databases and persistent data • Create and deploy production-grade architectures • Implement user authentication and authorization • Build AI-powered applications with LLMs • Develop dashboards, and visualizations • Implement security best practices About the technology With the Streamlit framework, you can build interactive web apps entirely in Python incredibly fast. By providing a collection of pre-built UI components and streamlined configurations, Streamlit turns your ideas for data tools and AI workflows into usable applications without any tedious HTML, CSS, and JavaScript. About the book Build Python Web Apps with Streamlit follows a proven learn-by-building approach. Each chapter introduces a new hands-on project. You’ll create data dashboards, interactive checklists, and even an AI chatbot optimized with RAG and agentic patterns. As you go, you’ll practice debugging, deployment, database integration and other software engineering skills. What's inside • Understand Streamlit’s execution model • Work with databases and persistent data • Implement user authentication and authorization • Build AI-powered applications About the reader For Python programmers. No web app or AI skills required. About the author Aneev Kochakadan is a software engineer at OpenAI, with prior experience at Stripe and Google. He has designed and built systems ranging from online transactional services to data pipelines and business intelligence tools. Table of Contents Part 1 1 Introduction to Streamlit 2 Getting started with Streamlit 3 Taking an app from concept to code 4 Streamlit’s execution model 5 Sharing your apps with the world Part 2 6 A dashboard fit for a CEO 7 The CEO strikes back: Supercharging the dashboard 8 Building a CRUD app with Streamlit Part 3 9 Creating an AI-powered application 10 RAG and agentic apps with LangGraph and Streamlit Part 4 11 Testing Streamlit apps 12 Packaging and deploying Streamlit apps Appendixes A Installing Python and Streamlit

The Streamlit framework empowers Python developers to build web applications quickly without writing a single line of HTML, CSS, or JavaScript. There’s no need to switch stacks just to add a web front-end to a Python application. This easy to use, lightweight toolkit is perfect for putting a friendly UI on an AI model or creating an interactive data dashboard.

Build Python Web Apps with Streamlit

De Gruyter Textbook

This book is a textbook about calculus of one variable. It contains proofs of all the theorems, propositions etc but also Python codes in order to solve many calculus problems. To produce these codes we use AI and show to the reader how to do that.&nbsp;
This book aims to supplement the existing bibliography concerning single-variable calculus. Material including Python code for both symbolic and approximate computations. Our goal is to appropriately use artificial intelligence to generate this code and demonstrate to students how this is approximately done. This book is extremely useful for first-year science students, their professors, and others.

This book is a textbook about calculus of one variable. It contains proofs of all the theorems, propositions etc but also Python codes in order to solve many calculus problems. To produce these codes we use AI and show to the reader how to do that.&nbsp;This book aims to supplement the existing bibliography concerning single-variable calculus. Material including Python code for both symbolic and approximate computations. Our goal is to appropriately use artificial intelligence to generate this code and demonstrate to students how this is approximately done. This book is extremely useful for first-year science students, their professors, and others.

with Python and AI

Calculus of One Variable

This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)—from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.
Starting from the essentials, you’ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You’ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level—an essential skill for scaling real-world LLMs. You’ll also gain mastery over the phases of training that define today’s leading models:
<ul>
<li>Pretraining - Building general linguistic and semantic understanding.

</li><li>Midtraining - Expanding domain-specific capabilities and adaptability.

</li><li>Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.

</li><li>Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.



The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.
By the end of this book, you’ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.
What You’ll Learn
<ul>
<li>How to configure and optimize your development environment using PyTorch

</li><li>The mechanics of tokenization, embeddings, normalization, and attention mechanisms.

</li><li>How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.

</li><li>How to integrate custom CUDA kernels to accelerate transformer computations.

</li><li>The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.

</li><li>Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.

</li><li>How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.





Who this book is for:
Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch.
</li></ul></li></ul>

Design, Train, and Deploy LLMs with PyTorch

Building Large Language Models from Scratch

Multitasking ist der Schlüssel zu leistungsfähigen Embedded-Systemen, und zugleich eine der größten Herausforderungen in der Mikrocontroller-Programmierung. Dieses Buch zeigt Ihnen Schritt für Schritt, wie Sie von einem einfachen Ein-Task-Programm zu einem strukturierten, reaktionsfähigen und skalierbaren System gelangen. Dabei steht nicht nur das Wie, sondern vor allem das Warum im Mittelpunkt.Sie lernen:<ul><li>warum scheinbare Parallelität entsteht</li><li>weshalb blockierende Programme instabil werden</li><li>wie Aufgaben effizient und kontrolliert parallel ablaufen</li>
 
 </ul>Ausgehend vom klassischen Superloop vermittelt das Buch moderne Multitasking-Strategien - von nicht-blockierender Programmierung und Tick-Systemen bis zu Task-Schedulern, Zustandsmaschinen und ereignisgesteuerten Konzepten.Der Fokus liegt auf der Praxis: Sensoren, Displays, Kommunikation und reale Anwendungen werden Schritt für Schritt integriert und mit verständlich kommentierten Beispielprogrammen erklärt.Fortgeschrittene Themen umfassen:<ul><li>Multicore-Programmierung mit dem Raspberry&nbsp;Pi&nbsp;Pico</li><li>asynchrones Programmieren mit uasyncio</li><li>RTOS-Grundlagen mit FreeRTOS</li><li>die Kombination aus Multicore und RTOS</li>
 
 </ul>Dabei werden auch die Grenzen dieser Techniken, insbesondere unter MicroPython, klar aufgezeigt.Das Buch richtet sich an ambitionierte Einsteiger und fortgeschrittene Entwickler, die Embedded-Systeme fundiert verstehen und robuste, wartbare Multitasking-Anwendungen entwickeln möchten.

Zeitsteuerung, Scheduler, PIO und Zustandsmaschinen

Multitask-Programmierung mit dem Raspberry Pi Pico

This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.
This book guides through the entire modern machine learning lifecycle. You’ll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You’ll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you’ll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you’ll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.
In the end, this book helps you build systems that are robust, auditable, and optimized—whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.
What you will learn:
Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.
Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.
Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.
Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.
Who this book is for:
This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.

This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You’ll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You’ll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you’ll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you’ll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimized—whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.&nbsp;

Practical Machine Learning and Agentic Workflows with Python and PyTorch

Building AI Systems with Python

Client Platform Engineers, device administrators, and others charged with managing large fleets of devices need to be able to wield open-source tools, conform them to their unique use cases, and get them on devices securely. This has traditionally been done with other languages, some of which have evolved since the early days of Unix - the early days of the very idea of an operating system. But the increasingly closed nature of Apple devices means that these skills need to move to Swift, and all the good and bad that comes with that.


This book is about how to build Swift applications that automate tasks on Apple devices. Things like pulling information down from a Mobile Device Management (MDM) solution to configure settings, deliver apps to devices, and gain telemetry into areas of the system that might not otherwise be available to legacy scripting tools that can't get entitlements to manage various aspects of systems. Traditional Swift books don't cover these scripty automation tasks, or how to build software that can't go on the App Store. Yet that's exactly what admins need to do. We want to make the world a more secure place, and this is one of the ways we get there.


 


What You'll Learn


 How to wield Swift for secure device automation tasks.

 How to convert your code into compiled scripts.

 How to build functional programs for device inventory.

 How to integrate with enterprise systems for seamless operation.

 How to master Mobile Device Management (MDM) with Swift.





 


Who This Book Is For


This book is for client platform engineers, device administrators, and anyone managing large fleets of Apple devices.

Client Platform Engineers, device administrators, and others charged with managing large fleets of devices need to be able to wield open-source tools, conform them to their unique use cases, and get them on devices securely. This has traditionally been done with other languages, some of which have evolved since the early days of Unix – the early days of the very idea of an operating system. But the increasingly closed nature of Apple devices means that these skills need to move to Swift, and all the good and bad that comes with that.This book is about how to build Swift applications that automate tasks on Apple devices. Things like pulling information down from a Mobile Device Management (MDM) solution to configure settings, deliver apps to devices, and gain telemetry into areas of the system that might not otherwise be available to legacy scripting tools that can’t get entitlements to manage various aspects of systems. Traditional Swift books don’t cover these scripty automation tasks, or how to build software that can’t go on the App Store. Yet that’s exactly what admins need to do. We want to make the world a more secure place, and this is one of the ways we get there.&nbsp;What You'll Learn<ul><li>How to wield Swift for secure device automation tasks.</li><li>How to&nbsp;convert your code into compiled scripts.</li><li>How to build functional programs for device inventory.</li><li>How to integrate with enterprise systems for seamless operation.</li><li>How to master Mobile Device Management (MDM) with Swift.</li></ul>&nbsp;Who This Book Is ForThis book is for client platform engineers, device administrators, and anyone managing large fleets of Apple devices.

An Introduction to Swift Scripting and Programming for Apple Admins

Mastering Swift

Dieses Buch führt Sie Schritt für Schritt in die faszinierende Welt der State Machines ein und zeigt, wie Sie Hardware direkt und effizient ansteuern können - jenseits klassischer CPU-gesteuerter Ansätze.In diesem Buch lernen Sie:<ul><li>LEDs und NeoPixel kreativ steuern: Blinken, Dimmen, Farbwechsel und komplexe Animationen</li><li>Taster und Encoder zuverlässig auslesen und in Echtzeit in Ihre Projekte integrieren</li><li>Analoge Eingänge (ADC) nutzen und Signale digital steuern</li><li>Servos präzise ansteuern und Bewegungsabläufe automatisieren</li><li>SPI mit PIO umsetzen, um Displays, Sensoren und Module anzusteuern</li><li>Menüsysteme und Submenüs auf SPI-Displays realisieren, inklusive Navigation über mehrere Taster</li><li>PWM, Signalgenerierung und Frequenzsteuerung für akustische oder visuelle Effekte</li><li>Debugging-Strategien und Tipps, um Ihre PIO-Programme effizient zu entwickeln</li>
 
 </ul>Alle Beispiele sind praxisnah, sofort umsetzbar und zuverlässig. Mit klaren Anleitungen, Hintergrundwissen und fertigen Codebeispielen lernen Sie, die PIO-Fähigkeiten des Pico maximal zu nutzen - egal ob für Maker-Projekte, Prototypen oder professionelle Anwendungen.Erleben Sie, wie Sie komplexe Hardwaresteuerung elegant mit dem Pico realisieren können - schneller, flexibler und leistungsfähiger als je zuvor.

Praxisnahe Projekte, Profi-Tricks und eigene Anwendungen

PIO-Programmierung mit dem Raspberry Pi Pico

Programmierung mit SCL und dem TIA Portal

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