day2 recap

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