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Praktische Leitfäden, Artikel und Werkzeuge für Ihre KI-Reise

Wir veröffentlichen fast jede Woche einen Artikel im Chelsea-AI-Newsletter darüber, was in produktiver KI funktioniert und was nicht. Einundvierzig davon sind hier nach vier Themen sortiert, jeweils mit einer Zusammenfassung, damit Sie vor dem Klick einschätzen können, ob sich die Lektüre lohnt. Darunter stehen die Bücher unseres Direktors und unsere Vorträge.

Nach Thema stöbern

Vier Themen ziehen sich durch alles, was wir veröffentlichen. Jedes sammelt die Artikel zu einem Teil unserer Arbeit.

Bücher unseres Teams

Unsere umfassenden Schulungsprogramme basieren auf praktischer Implementierungserfahrung, die in Bestseller-Büchern unseres Teams vorgestellt wird

Cover of Retrieval Augmented Generation, The Seminal Papers

Retrieval Augmented Generation, The Seminal Papers

Published: March 2026 | New Release

Principles for architecting reliable and verifiable AI

Retrieval Augmented Generation (RAG) is a standard process for grounding LLM prompts in user-specified content rather than relying only on a model’s training data. RAG has grown from a simple prompt engineering workflow into a sophisticated set of data analysis, storage, and retrieval techniques. Retrieval Augmented Generation, The Seminal Papers explores foundational research papers that explain why RAG works, how it’s built, and what makes it different from other approaches.

Focus areas include:

Retrieval Augmented Generation AI Architecture Verifiable AI
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Cover of Generative AI with LangChain

Generative AI with LangChain (2nd edition)

Published: May 2025 | Amazon Bestseller in Programming

Build production ready LLM applications and advanced agents using Python and LangGraph

A practical guide to leveraging LangChain and LangGraph for GenAI implementation, with real-world examples ranging from customer support to data analysis. The 2025 edition features updated code examples and improved GitHub repository.

Focus areas include:

Enterprise-grade LLM application architecture Prompt engineering best practices RAG implementation for knowledge augmentation Custom agent development Production deployment strategies
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Cover of Machine Learning for Time Series

Machine Learning for Time Series

Published: October 2021 | Industry Standard Reference

Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods. This comprehensive guide covers everything from data preprocessing to advanced models for time-dependent data. The included tutorials range from simple forecasting to complex deep learning architectures for time series analysis.

Focus areas include:

Anomaly detection systems Forecasting methodologies Feature engineering for time-series Deep learning approaches Production deployment patterns Time series preprocessing techniques LSTM and RNN architectures
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Cover of Artificial Intelligence with Python Cookbook

Artificial Intelligence with Python Cookbook

Published: October 2020 | BookAuthority Best-Seller

Proven recipes for applying AI algorithms and deep learning techniques

Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow and PyTorch. The practical cookbook approach provides ready-to-use solutions for common AI challenges, from computer vision to natural language processing, with complete code examples and detailed explanations of implementation considerations.

Focus areas include:

Deep learning fundamentals Computer vision applications NLP implementation techniques Reinforcement learning Model optimization strategies Transfer learning approaches Hyperparameter tuning
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Amazon-Autorenseite ansehen

Vorträge & Bildung

Moving Beyond Statistical Parrots: LLMs and Their Tooling

ODSC 2024, Data Science Week Amsterdam

Focus: Open-Source-LLMs und Unternehmensimplementierung

Time-Series in Python: Preprocessing and ML

ODSC 2022

Focus: Machine-Learning-Techniken für Zeitreihendaten

Strategic AI Implementation

Data & Analytics Summit EU 2022

Focus: KI-Strategie und Implementierungs-Roadmaps für Unternehmen

Future of Data Science and LLMs

PyData London 2023

Focus: Podiumsdiskussion über Open-Source-KI-Technologien

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