Maintainer: @simeneide
Welcome to the course material for the internal AI Academy that we have run in Schibsted for two years now. Due to popular demand by former colleauges, we decided to open source the material, so everyone can use it and learn more AI. Contributions are welcome, and if you use it in your organization, please give us a shout :)
We mainly follow the very popular AI course fast.ai, but have made some modifications and exercises to make it more relevant to our products and way of working. That means that in addition to lessons by Jeremy Howard, we will provide lab sessions where the students will discuss and train their own AI models with the help of instructors from Schibsted.
The course begins by understanding how to apply deep learning. As the course progresses, the student will dive into more details and get a deeper understanding of AI and deep learning, as well as more practical experience in building machine learning models.
- Target audience: Software engineers, data engineers and other technologists with some coding experience.
- Expected outcome: A good understanding of AI and deep learning. At the end of the course, the student is able to build AI models and understand a rudimentary way of deploying them.
- Course set-up: The students follow a training program that runs over 14 weeks. The estimated required effort is around 4-6 hours/week. At the end of the course, the students will present an AI project related to their interests or daily work.
When the course starts, we will divide you into groups.
In the lessons/ folder, you can find all info about how to prepare for the lab sessions, and what to do during the lab sessions.
We recommend working together as a group on all parts of the course (preparation + sessions + project). If parts of the material is unclear, discuss it with your group, and/ or post a question on slack.
During the lab session, you will be able to work on the code together with your group, and get help to understand what you did not understand in the lecture and/or notebook earlier.
We also recommend finishing the lab after the lab session, if there is not enough time during the session to complete the notebook.
Remember that you can always ask questions on the slack group if you have any!
One of the weeks will cover ethics. This week might differ slightly, as you will be given an introduction to Schibsteds FAST framework (not to be confused with Fast AI).