A Transformer is a model architecture that eschews recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of attention mechanisms allows for significantly more parallelization than methods like RNNs and CNNs.
Visualizing and Explaining Transformer Models From the Ground Up - Deepgram Blog ⚡️
Hermetic Liquid Immersed Electrical Transformer Explained - saVRee
What Is the Transformer Architecture and How Does It Work?
Illustrated Guide to Transformers- Step by Step Explanation, by Michael Phi
What Is a Transformer Model?
Transformer Tap Changers: Basic Principles and Testing Explained - Articles - TestGuy Electrical Testing Network
Ideal Transformer - Explained with Schematics and equations
Detection Transformer (DETR) Explained : r/neuralnetworks
The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.
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