Description

📚 Premium Notebook Series: Anomaly Detection Journey

Take a deep, hands-on journey into anomaly detection with a professional-grade, real-world-ready notebook series.

This premium collection is built for learners, engineers, and researchers who want to move beyond toy examples and shallow tutorials and truly understand, experiment, and apply advanced anomaly detection methods in realistic industrial scenarios.

Start your journey into real anomaly detection today and build skills that will stay with you for your entire ML career.

See It First — Public GitHub Demo

To give you a real look at the quality and depth of this series, a restricted content demo is available publicly:

• Includes the complete VAE track (code + notebooks)

• Shows the actual project structure and learning approach

• Available here:
  [https://github.com/olivergrau/anomaly-detection-notebook-series-demo]

This way, you can explore the material before committing and get a genuine feel for the hands-on experience awaiting you.

What’s Included

• 4 Complete Notebook Tracks:

  • Variational Autoencoders (VAEs): Learn to model normal patterns and detect subtle anomalies via reconstruction.

  • PatchCore: Implement efficient, one-class anomaly detection without relying on dense supervision.

  • DRAEM: Explore reconstruction-based segmentation for precise anomaly localization.

  • MLOps Operationalization: Deploy your models in production environments using MLflow, FastAPI, Render, and GitHub Actions.

• Real-World Dataset Challenge:

  • Work on the AITEX dataset, a highly challenging and industry-like dataset — significantly tougher and more realistic than standard benchmarks like MVTec.

• Production-Ready Source Code:

  • Full codebases for model training and inference included.

  • Organized, reusable modules ready for your own research or real-world projects.

• Truly Hands-On Learning:

  • Model weights are not provided:  encouraging you to fully train, troubleshoot, and understand each model yourself.

  • Experience-based learning: you will iterate, debug, and build real skills, not just run code.

• Technology Stack:

  • PyTorch (with GPU acceleration recommended)

  • scikit-learn, scipy, and modern Python libraries

  • All examples in Jupyter Notebooks for easy interactive learning.

Why This Series is Different

This series isn’t about “running a few cells”.  It’s about building deep intuition, experimenting, and developing a robust engineering mindset around machine learning for anomaly detection.

• It’s serious and it can take weeks to work through, and that’s by design.

• It reflects a real learning journey! It is created by someone who tried, failed, refined, and now shares the results.

• It’s intended for those who want to truly grow, not just check off another tutorial.

Whether you’re a self-learner, a working engineer, or a researcher bridging theory and practice, these notebooks will give you the foundation and confidence to design, train, and deploy anomaly detection systems at a professional level.

“This product is licensed for personal, non-commercial use only. See LICENSE.txt for full details.”