amaigmbh

Just as we promised, we now updated and expanded the "Deep Learning" section. Check it out!

As for our opinion (which is just that, an opinion) why we think that the statistical foundations and knowledge about more traditional algorithms is important, it's based on the business needs and our experience. While it might seem less necessary if the goal is to "learn deep learning", it is highly relevant if your task is to "solve this business problem".

Our perspective is the industrial one. And while there are certainly many complex business problems where deep learning is required, there are more cases where a traditional approach is sufficient and actually the better solution (e.g. due to the memory footprint, latency or other reasons). We routinely work on both kinds of problems on a day to day basis, but we would never go straight to deep learning approaches if simpler and faster traditional methods comprise a better solution in a given use case. So, our employees are expected to know both and to be able to judge when to apply which approach.

You are right that the "Deep Learning" section is rather shallow up to now. We are currently working on expanding it to offer a more comprehensive view of the field and expect to release this update next week. Stay tuned! :)

There are many areas of AI and we have started with machine learning. You are right, a clarification would be helpful here. We are constantly striving to improve and expand this roadmap. It would be fantastic if you had recommendations for further paths we could draw. The repo is also open for contributions, so feel free to add.