LLM Engineering & Generative AI · Tutorial tracks

Applied AI engineering

LLM APIs, structured outputs, retrieval, evaluation harnesses — Python-native application focus.

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Lesson outline

  1. 217
    Learning Rate Schedulers

    Use learning rate schedulers — Applied AI engineering.

  2. 218
    Batch Size Effects

    Experiment with batch size effects — Applied AI engineering. Learn hands-on steps, compare options, troubleshoot edge cases, and see what's next.

  3. 219
    Initialize Weight for Faster Convergence

    Learn how to initialize weights for faster convergence in neural networks. This lesson covers the problem, key concepts, step-by-step methods, and a hands-on exercise to improve model training speed and stability.

  4. 220
    Regularize with L1 and L2

    Regularize with L1 and L2 penalties in Python. Learn how L1 (Lasso) and L2 (Ridge) penalties prevent overfitting, and apply them in a hands-on exercise for applied AI engineering.

  5. 221
    Elastic Net for Feature Selection

    Use elastic net for feature selection in Applied AI engineering. Learn how to apply elastic net in a hands-on exercise, compare options, and tackle edge cases. Step-by-step tutorial for developers.

  6. 222
    Implement SMOTE-ENN

    Implement SMOTE-ENN for imbalance in this Applied AI engineering tutorial — hands-on steps, troubleshooting, and what to study next.

    Applied AI engineering — step-by-step tutorials

    What you will find here

    This track walks through applied ai engineering in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.

    Tutorials vs quizzes and code samples

    Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.