Transformer Architecture
Transformer architecture refers to the specific neural network design uncovered and implemented by Google's AI team, Google Brain.
Superseding its predecessor architecture known as RNN (Recurrent Neural Networks), transformer architecture is an adaptive approach to data processing. Rather than sequencing steams of information in chronological strings, transformer architectures process entire blocks of data simultaneously and substitute form factors to optimize data coherence.
Rooted in identifying core concepts by accurately allocating its focus, transformer architecture displaces excessive processing by applying four components: Attention Mechanisms, Multi-head Attention, Feed-Forward Layers, and Normalization Layers.
ChainGPT makes good use of the transformer architecture in the design of its AI by allowing users the ability to provide theoretically unlimited input requests and being able to handle them aptly.
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