Machine Learning is an empirical science: Try. Optimize. Repeat.
Model: is like a student; training = teaching and then is tested. Loss = how ‘wrong’ the student is when tested.
Regression (predicting values): Mean Squared Error (MSE – penalizes large errors), Mean Absolute Error (MAE – all errors are equal, less sensitive to outliers). Both MSE and MAE contain Y (the ground truth) and Y-hat (the prediction).
Classification (predicting categories): Binary cross entropy (binary classification), categorical cross entropy (multi-class category).
ML models are simpler: linear regression, decision trees, random forests.
DL models are more complex (multi-layered neural networks): input/hidden layers/output. They need high computational power.
Training process and loss: random initialisation, forward pass, loss computation, backward pass, parameter update, repeat. Student learns, teacher corrects, repeat.
CNN (convolutional): find patterns in spatial hierarchies. The filter slides (‘convolutes’) across the input. Few parameters
DNN (dense): fully connected layers, many parameters
Transformer: attention mechanisms (relationships between words)

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Written by Dorin Moise (Published articles: 284)
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