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#MachineLearning #DataScience #NLProc

Code:

How positional encoding looks once it has been applied to an embedding

Theory:

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:👇

Code:

Theory:

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

Code:

Theory:

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

Coding up the layers:

Theory:

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

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

code:

Step by step run through

Visual mathematical breakdown

Snippets. Intuition behind self-attention