Machine Learning

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Across
  1. 2. : Learning rule based on co-activation of neurons
  2. 4. : Matrix decomposition used in ML
  3. 6. : Function introducing non-linearity in neurons
  4. 7. : Group of connected neurons
  5. 11. : When a model performs poorly on all data
  6. 13. : Core process of machine intelligence
  7. 14. : Root Mean Squared Error
  8. 15. : Rule for learning using output error
  9. 16. : True positives out of actual positives
  10. 20. : Technique to reduce overfitting in regression
  11. 21. : Additional term to shift activation threshold
  12. 22. : Phase where the model learns from data
  13. 25. : Dataset used to fine-tune model performance
  14. 26. : True positives out of predicted positives
  15. 28. : Network with multiple hidden layers
  16. 29. : Alternative name for delta learning rule
Down
  1. 1. : Evaluation phase with unseen data
  2. 3. : Algorithm to tune weights in neural networks
  3. 5. : Matrix transformation to simplify structure
  4. 8. : Value that indicates direction and scale change in matrices
  5. 9. : Quantity having both direction and magnitude
  6. 10. : Basic neural network model
  7. 12. : Estimating future outcomes based on data
  8. 17. : Table to visualize the performance of classification
  9. 18. : When a model memorizes data, fails on new data
  10. 19. : Unsupervised technique to group similar items
  11. 23. : Metric of how often the model is correct
  12. 24. : Classification algorithm using sigmoid function
  13. 27. : Type of network inspired by biological brain