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. youtu.be/LSCsfeEELso #ArtificialIntelligence Info:👇

In this Transformers from scratch episode, we look at how we can build and code up the Encoder Layer in its entirety: youtu.be/z5eymymeMnU #ArtificialIntelligence #NLProc #MachineLearning #DataScience

Transformers: Residual Connections, Layer Normalization, and Position Wise Feedforward Networks In this episode, we take a look the components shared across all transformer sublayers youtu.be/Wc8qJxoD52g #ArtificialIntelligence #nlproc

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. youtu.be/Y4qft_HoPUw #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. youtu.be/1BFE1Tfs8tM #NLProc #ArtificialIntelligence #ML #Data