Across
- 5. Parameters that determine the importance of inputs in neural networks
- 6. Learning with labeled training data
- 7. S-shaped activation function with formula 1/(1+e^-z)
- 9. Basic unit of neural networks, inspired by brain cells
Down
- 1. Type of regression algorithm used for classification problems
- 2. Unsupervised learning technique for discovering patterns in unlabeled data
- 3. ML technique for predicting continuous values like temperature or sales
- 4. ML technique for discrete values that can be counted, not measured
- 8. Popular clustering algorithm mentioned in the presentation