Welcome back to my video series on the Transformer! In this video, I'll be discussing the Transformer in more detail, building on the foundations laid in my previous video. I received a lot of positive feedback on the first video, but some viewers noted that the audio quality wasn't great. I've taken that feedback to heart and improved the audio for this video. If you haven't seen the first video, don't worry - you don't need to in order to follow along with this one.
Before we dive into the Transformer, I want to take a moment to talk about recurrent neural networks (RNNs). RNNs were the go-to solution for many sequence-to-sequence tasks before the Transformer was introduced. They work by taking in a sequence of inputs and outputting another sequence of outputs, with the output at each time step being determined by the input at that time step and the hidden state from the previous time step.
However, RNNs have some limitations. For one, they can be slow for long sequences, as the computation has to be done sequentially for each token in the input. Additionally, RNNs can suffer from the vanishing or exploding gradients problem, in which the gradients become either very small or very large during training, making it difficult to learn the weights effectively.
The Transformer addresses these limitations by using a different approach to sequence-to-sequence tasks. Instead of processing the input sequentially, the Transformer processes the entire input in parallel using self-attention mechanisms. This allows it to handle long sequences more efficiently and avoid the vanishing or exploding gradients problem.
The Transformer is made up of two main components: the encoder and the decoder. The encoder takes in the input sequence and generates a sequence of continuous representations, which are then passed to the decoder. The decoder generates the output sequence one token at a time, using the output from the encoder and the previous tokens it has generated.
Let's take a closer look at the encoder. It starts with input embeddings, which map each input token to a continuous vector representation. These embeddings capture the meaning of the word, as well as its position in the sentence using positional encodings. The positional encodings give the model information about the relative positions of the words in the sentence, which is important for understanding the meaning of the sentence.
Next, the encoder uses multi-head attention to allow the model to relate words to each other in the input sequence. Multi-head attention is an extension of self-attention, which allows the model to focus on different aspects of the input when generating the output. The encoder applies multi-head attention to the input sequence multiple times, allowing it to capture complex relationships between the words.
Finally, the encoder uses position-wise feed-forward networks to apply a non-linear transformation to each position in the input sequence independently. This allows the model to capture any additional patterns in the input that the attention mechanisms may have missed.
The decoder follows a similar structure to the encoder, but with an additional layer of masked multi-head attention. This masking ensures that the decoder only pays attention to the input sequence and the tokens it has generated so far, preventing it from seeing future tokens in the output sequence.
In summary, the Transformer is a powerful model for sequence-to-sequence tasks that uses self-attention mechanisms to process the entire input sequence in parallel. It avoids the limitations of RNNs by processing the input efficiently and avoiding the vanishing or exploding gradients problem. The encoder and decoder work together to generate the output sequence, with the encoder capturing the meaning of the input sequence and the decoder generating the output one token at a time.
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