> For the complete documentation index, see [llms.txt](https://chi-education.gitbook.io/chi-edge-or-education/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://chi-education.gitbook.io/chi-edge-or-education/chi-edge-education/module-i-autonomous-vehicles/pathways/regular-pathway/background-information/machine-learning.md).

# Machine Learning

An Overview of Machine Learning and Additional Resources for Exploration

## General Information

Machine learning is the creation of systems capable of learning and improving from "experience" and a subset of artificial intelligence. This means that instead of having to be coding a system to be able to perform a certain task in a certain way, the system learns from examples in a way similar to humans (hence artificial intelligence). 

These examples can be images, words, audio, video, or a whole range of other possibilities. To read more, here is a an article on the subject published by [MIT.](https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained)

## End-to-End Deep Learning

In this project, CHI\@Edge [Autonomous Cars with Donkeycar](https://www.donkeycar.com/), end-to-end deep learning is utilized. While E2E (end-to-end) deep learning is a complex topic, it can essentially be boiled down to not decomposing. This refers to how machine learning problems are usually decomposed into sub-problems. 

However, E2E deep learning allows for a simplification of the learning process by having input passed to a neural network and receive an output, with relevant details abstracted by the network. For more technical details and discussion about this topic, a question called [What does end-to-end training mean](https://ai.stackexchange.com/questions/16575/what-does-end-to-end-training-mean) was posted on the AI Stack Exchange.