Materialien
Sammlung weiterer Materialien zu den Themen des Moduls.
Literatur
- Géron, Aurélien (2022): Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media, Inc..
- Frochte, Jörg (2020): Maschinelles Lernen: Grundlagen und Algorithmen in Python, Carl Hanser Verlag GmbH Co KG.
- LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey (2015): Deep learning, Nature Publishing Group UK London.
- Alammar, Jay and Grootendorst, Maarten (2024): Hands-on large language models: language understanding and generation, O’Reilly Media, Inc..
- Situnayake, Daniel and Plunkett, Jenny (2023): AI at the Edge, O’Reilly Media, Inc..
- Russell, Stuart Jonathan and Norvig, Peter (2012): K{"u}nstliche Intelligenz: Ein moderner Ansatz, Pearson Deutschland GmbH.
MOOCs
Massive Open Online Courses.
- https://www.deeplearning.ai/courses/
- https://www.coursera.org/specializations/deep-learning
- https://www.datacamp.com/
- https://www.edx.org/
Webseiten
- Strategy for DL Troubleshooting - Summer School der Uni Berkeley, 2021
- How LLMs Actually Generate Text (Every Dev Should Know This)
- Youtube Channel von Andrej Karpathy
- Official code repo for the O’Reilly Book - “Hands-On Large Language Models”
- Official code repository for the book Build a Large Language Model (From Scratch)
- Stanford CS336 Language Modeling from Scratch I 2025
- Stanford CS224N: Natural Language Processing with Deep Learning
- MIT Deep Learning
-
[Stanford CS230 Autumn 2025](https://www.youtube.com/watch?v=DNCn1BpCAUY) - Introduction to Edge AI
- https://www.stateof.ai/
Tools
- https://www.tensorflow.org/
- https://pytorch.org/
- https://scikit-learn.org/stable/index.html
- https://github.com/jupyterlab/jupyterlab
- https://www.anaconda.com/
- https://flowiseai.com/
- https://n8n.io/
- https://ollama.com/
- https://github.com/google-gemini/gemini-cli
- https://jules.google.com/
- https://www.kaggle.com/
- https://elevenlabs.io/
- https://alexlenail.me/NN-SVG/LeNet.html
- https://github.com/lutzroeder/netron