This text describes an experiment where a machine learning app was created to decode secret baseball signs used in the game. The author, along with a friend named Jabril, developed the app to predict when the opposing team would steal a base based on the signals given by coaches. They collected data from real games, cracked the code for one team's signals, and demonstrated the app's effectiveness. The text also provides a simplified explanation of machine learning and neural networks.
Sure, here are the key facts extracted from the text without including opinions:
1. The author came up with an idea for an app to decode baseball signs two years ago.
2. The app is designed to predict when the other team is going to steal a base.
3. The author and their friend Jabril developed the app.
4. In baseball, coaches give signs to the batter and base runner, such as whether to bunt or steal.
5. Stealing a base involves running as soon as the pitch is thrown.
6. Coaches use indicators before giving the real sign, and the app decodes these signs.
7. The app uses machine learning to make predictions based on sequences of signs.
8. The app successfully decoded a complex steal sign in a real-world test.
9. Neural networks in machine learning are compared to how the human brain draws boundaries.
10. Machine learning can solve complex problems faster than traditional methods.
11. The machine learning model successfully decoded a complex set of signs.
12. The author's friend filmed third-base coaches to collect data for the machine learning model.
13. The author used discreet methods like a GoPro in a cup to film signs.
14. The app allowed the author to predict baseball signs and gain an advantage in the game.
15. The author provided links to both versions of the app in the video description.
Please note that these facts are presented in a summarized form and do not include opinions or additional context.