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Unsupervised learning looks for patterns without being told what to look for. Typical jobs are clustering similar items, reducing dimensions, and flagging anomalies. There is no accuracy score in the usual sense, so evaluating the result takes judgement. In practice: Segmenting customers into groups nobody defined in advance, based only on purchase behaviour.
Validation data sits between training and test. You use it to choose hyperparameters and to spot overfitting early, because the model never learns from it directly. Reusing it too aggressively leaks information and quietly makes it a second training set. In practice: If validation loss starts rising while training loss keeps falling, you are overfitting — stop.
Weights determine how much each input contributes to each output at every layer. They start random and are updated by gradient descent until the loss stops improving. When people talk about ‘open weights’, they mean the trained numbers are downloadable, which is what makes local and self-hosted use possible. In practice: Downloading an open-weights model means downloading a file of billions of floating-point numbers.