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[feather @feather_ai]

Tired of spending time and money on copywriting and editing images?

https://t.co/uLQIJ14Wb0

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#MachineLearning #DataScience #NLProc

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Positional Encoding allows us to inject information about the order of the sequence into an otherwise position-indifferent architecture. The original Transformer does this by injecting sinusoids into the word embeddings.

https://t.co/toAhkzsnAj

#ArtificialIntelligence

Info:👇

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Code:

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Code:

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In this Transformers from scratch episode, we look at how we can build and code up the Encoder Layer in its entirety:

https://t.co/Tgw0MXVwRr

#ArtificialIntelligence #NLProc #MachineLearning #DataScience

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Theory:

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Code:

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Transformers: Residual Connections, Layer Normalization, and Position Wise Feedforward Networks

In this episode, we take a look the components shared across all transformer sublayers

https://t.co/Ic8xtqJC72

#ArtificialIntelligence #nlproc

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Theory:

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Coding up the layers:

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Transformers: Multi-head attention

In this episode/lesson, we look at upping the self-attention mechanism we looked at previously to the multi-head variant. Includes the theory and code.

https://t.co/j9jdQPUu1O

#NLProc #ArtificialIntelligence #MachineLearning #DataScience

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Theory:

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Transformers: Self-attention

In this video tutorial series, we take a look at the heart of the Transformer, the self-attention mechanism. After an intuitive breakdown of how it works, we code up the mechanism.

https://t.co/Q7ygAhBjy3

#NLProc #ArtificialIntelligence #ML #Data

[feather @feather_ai]

code:

[feather @feather_ai]

Transformers: Self-attention

In this video tutorial series, we take a look at the heart of the Transformer, the self-attention mechanism. After an intuitive breakdown of how it works, we code up the mechanism.

https://t.co/Q7ygAhBjy3

#NLProc #ArtificialIntelligence #ML #Data

[feather @feather_ai]

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