The second video about the Transformer is intended to improve the audio quality of the first video and cover the same content with some improvements.
Recurrent neural networks were slow for long sequences, had issues with vanishing or exploding gradients, and had difficulty accessing information from long time ago. The Transformer was developed to address these issues.
The two main blocks of the Transformer are the encoder and the decoder.
Beddings capture the meaning of the word, while positional encoding provides information about the position of the word in a sentence. Together, they help the self-attention mechanism relate words to each other in the input sequence.
The self-attention mechanism is calculated by multiplying the Q matrix (input sentence) by the transposed K matrix (same input sequence), dividing by the square root of the dimension size, and then applying softmax to get a new Matrix. This new Matrix represents the dot product of each word with the embeddings of other words in the sentence, effectively capturing the relationship between them.
Batch normalization calculates the mean and variance of the same feature for all items in a batch, while layer normalization calculates the mean and variance of all features for each item in the batch independently. This allows layer normalization to treat each item in the batch independently, which can be beneficial for the Transformer model.
The Transformer model achieves this by replacing the values in the output of the softmax calculation formula with minus infinity for the interactions it doesn't want. This ensures that the softmax will replace these values with 0, effectively preventing those words from interacting with each other.
The start of sentence and end of sentence tokens are special tokens in the Transformer model's vocabulary that indicate the start and end position of a sentence. They are used to prepare the input of the decoder and the output of the model, and are essential for the model to understand the structure of the input sequence.
During training, the Transformer model learns to convert an English sentence into an Italian sentence, while during inference, the model maps an English sentence into an Italian sentence. The training stage involves backpropagating the loss to all the weights, while the inference stage involves producing the output token by token.