Deep Learning

Transformers

Attention lets every position look at every other in parallel.

Instead of passing a hidden state along a chain, each token computes a weighted view of every other token. Because there is no recursion, whole sequences train in parallel — which is what made scaling possible.

Position must be injected explicitly, and attention cost grows quadratically with sequence length, which is why so much recent work targets longer contexts specifically.

Also in Deep Learning

CNN RNN LSTM Auto Encoders Deep RL Epochs

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