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The easiest approach to give more importance to a group of specific training instances is simply using a weight to increase the error loss computed on those specific instances during training. Libraries like sklearn have implemented off the shelf the sample_weight parameter to perform precisely this. The obvious downside of this approach is that you need to ...


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How/why do we achieve the same/similar results though we are skipping a layer altogether Dropout is not a layer, even tough deep learning libraries implement it as a layer module for convenience. Why do we achieve same results? We don't, that's why dropout is applied only during training and not during test. And the fact that results change is also the core ...


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Regarding your first code snippet, there is no weight storing or continuation of training between the different CV folds whatsoever: each model is trained anew with the respective training data of each fold and validated on the validation data. Notice that this is exactly the idea behind cross validation - models trained on different folds are completely ...


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Epoch is defined as a full training pass over the entire dataset such that each example has been seen once. Thus, an epoch represents N/batch_size training iterations, where N is the total number of examples. Whereas a training step is one gradient update. In one step batch_size examples are processed. So, Number of training steps per epoch: ...


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Here is a quick idea: first calculate the count of how many times each word occurs in these documents (I don't know whether to lowercase them or not, do interface and Interface mean different things?), and sort them in the descending order of occurrence. Most frequent words can be called "keywords" of your configurations (such as vlan), or maybe ...


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