Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Changed Animal notebook link #221

Merged
merged 2 commits into from
Jul 14, 2023
Merged

Changed Animal notebook link #221

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
ba35158
updated animal.ipynb link
BethanyJep Jul 11, 2023
a3c6b81
Merge branch 'microsoft:main' into main
BethanyJep Jul 11, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion lessons/2-Symbolic/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,7 @@ Expert systems can be implemented using different tools:

## ✍️ Exercise: Animal Inference

See [Animals.ipynb](Animals.ipynb) for an example of implementing forward and backward inference expert system.
See [Animals.ipynb](https:/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) for an example of implementing forward and backward inference expert system.

> **Note**: This example is rather simple, and only gives the idea of how an expert system looks like. Once you start creating such a system, you will only notice some *intelligent* behaviour from it once you reach certain number of rules, around 200+. At some point, rules become too complex to keep all of them in mind, and at this point you may start wondering why a system makes certain decisions. However, the important characteristics of knowledge-based systems is that you can always *explain* exactly how any of the decisions were made.

Expand Down