subtitles/en/04_the-transformer-architecture.srt

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- Let's study the
transformer architecture.

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This video is the introductory
video to the encoders,

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decoders, and encoder-decoder
series of videos.

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In this series,

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we'll try to understand what
makes a transformer network,

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and we'll try to explain it
in simple, high-level terms.

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No advanced understanding of
neural networks is necessary,

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but an understanding of basic
vectors and tensors may help.

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To get started,

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we'll take up this diagram

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from the original transformer paper,

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entitled "Attention Is All
You Need", by Vaswani et al.

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As we'll see here,

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we can leverage only some parts of it,

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according to what we're trying to do.

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We want to dive into the specific layers,

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building up that architecture,

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but we'll try to understand
the different ways

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this architecture can be used.

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Let's first start

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by splitting that
architecture into two parts.

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On the left we have the encoder,

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and on the right, the decoder.

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These two can be used together,

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but they can also be used independently.

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Let's understand how these work.

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The encoder accepts inputs
that represent text.

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It converts this text, these words,

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into numerical representations.

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These numerical representations

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can also be called
embeddings, or features.

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We'll see that it uses the
self-attention mechanism

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as its main component.

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We recommend you check out the video

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on encoders specifically to understand

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what is this numerical representation,

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as well as how it works.

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We'll study the self-attention
mechanism in more detail,

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as well as its bi-directional properties.

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The decoder is similar to the encoder.

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It can also accept text inputs.

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It uses a similar
mechanism as the encoder,

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which is the masked
self-attention as well.

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It differs from the encoder

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due to its uni-directional feature

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and is traditionally used in
an auto-regressive manner.

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Here too, we recommend you
check out the video on decoders

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especially to understand
how all of this works.

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Combining the two parts

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results in what is known
as an encoder-decoder,

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or a sequence-to-sequence transformer.

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The encoder accepts inputs

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and computes a high-level
representation of those inputs.

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These outputs are then
passed to the decoder.

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The decoder uses the encoder's output,

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alongside other inputs
to generate a prediction.

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It then predicts an output,

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which it will re-use in future iterations,

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hence the term, auto-regressive.

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Finally, to get an understanding

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of the encoder-decoders as a whole,

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we recommend you check out
the video on encoder-decoders.

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