![]() ![]() If the sequences have different lengths, dynamic memory allocation is required, which can be more complex and less efficient. Memory allocation: Neural networks often allocate memory based on the maximum length of the input sequences.To enable sharing of parameters, all input sequences must have the same length. ![]() For example, in recurrent neural networks (RNNs), the same weights are used to process each time step. Sharing of parameters: In many neural network architectures, model parameters (weights) are shared across different parts of the input sequence.To perform matrix operations efficiently, all input instances must have the same shape. The data is organised into matrices, with each row representing an input instance. Matrix operations: Neural networks typically process inputs in batches, and batch processing is most efficient when the input data has a uniform shape.Inputs of the same length simplify the data processing pipeline and enable efficient batch processing. This requirement arises from the structure and operation of the underlying computational graph. In many machine learning models, including neural networks, the inputs are expected to have a fixed size or shape. Then I replace these rare words with “” so that my model can also learn to react to unknown words. Now you might ask how do you include this OOV token in the training data? I do this by automatically searching my dataset at the beginning for words that occur only 1 time. The model will then treat it like any other token and generate a response based on its learned behaviour. In this case, an unknown word can be replaced by the token “” before it is passed to the model. During training, the model learns to generate this token or to handle it accordingly. It is therefore advisable to train the model with regard to unknown words. This can be a personal name, a spelling mistake or something similar. With a translator, it is only a matter of time before an unknown word is entered. Frequently used loss functions and optimisers. ![]()
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