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Referenced Embeddings are numerical representations of high-dimensional data like text and images, transformed into lower-dimensional vectors that machine learning models can process efficiently. These vectors capture semantic relationships, so similar items are placed closer together in the em...

AI hidden states are vectors that represent the intermediate memory of a neural network, particularly recurrent neural networks (RNNs) and Transformers. In an RNN, the hidden state vector is computed at each time step, combining the current input and the previous hidden state to carry information...

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One-hot encoding is a process used to convert categorical data into a numerical format that machine learning algorithms can understand. It works by creating a new binary column for each unique category in the original data. Each new column contains a value for that category and for all others. [1...

Backpropagation, short for "backward propagation of errors," is a fundamental algorithm used to train artificial neural networks, particularly in the context of deep learning. It's a key component in enabling neural networks to learn from data and improve their predictive accuracy over time. [1] ...

In a Transformer model, a hidden state is the output representation of the input tokens after they have been processed by a layer. Unlike in Recurrent Neural Networks (RNNs), where a hidden state carries a sequential memory, each hidden state in a Transformer is a vector that represents the comb...

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Access Google Docs with a personal Google account or Google Workspace account (for business use).