> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/einfuehrung/woche-6-overfitting-und-regularisierung.md).
# Week 7 - Overfitting and Regularization
### This week we will...
cover the following topics:
* Important terms in machine learning
* Overfitting and regularization
* Model quality criteria
* Introduction to neural nets
### Learning Resources
{% file src="/files/gBfTuKPrY3cTAwToFWhs" %}
* [Graphical tool](https://playground.tensorflow.org/) for the definition and estimation of neural networks for different example datasets
* [Example ](https://github.com/opencampus-sh/einfuehrung-in-data-science-und-ml/blob/main/overfitting.ipynb)of the effect of overfitting and regularization
### Until next week you should...
* [x] watch the videos of the sections “[Neural networks intuition](https://learn.deeplearning.ai/specializations/machine-learning/lesson/uyfti/welcome!)” and “[TensorFlow implementation](https://learn.deeplearning.ai/specializations/machine-learning/lesson/v1elo/inference-in-code)” from week 1 of the course Advanced Learning Algorithms from DeepLearning.AI
* [x] further extend the dataset with additional variables that could be relevant for estimating revenue.
* [x] further test your baseline model's predictive performance on Kaggle!
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/einfuehrung/woche-6-overfitting-und-regularisierung.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.