Positional Encoding Explained - Sin, Cos, Encoding, Transformer - Advantages | Variants

In this video, we understand Positional Encoding in Transformers in a simple and intuitive way. This concept is very important because Transformers process data in parallel and do not naturally understand the order of words like RNNs or LSTMs. Here is the GitHub repo link: https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. We begin by understanding why positional encoding is needed. In language, word order is very important. Example Ind beats NZ NZ beats Ind Both sentences have the same words but completely different meanings because of word order. Previous architectures like RNN and LSTM handle order by processing words one by one (time step wise). Because of this sequential nature, they cannot process data in parallel. Transformers process all tokens at once, so they need a way to understand position. This is where positional encoding comes into the picture. What is Positional Encoding We create a unique vector for each position in the sequence and add it to the embedding of the word at that position. Example Word embeddings W, A, L, T Position vectors P1, P2, P3, P4 Final representation W + P1 A + P2 L + P3 T + P4 Properties of Positional Encoding Each position must have a unique vector Dimension of positional vector must match embedding dimension Values should be small so original meaning of embeddings is not disturbed Methods to Create Positional Encoding Learned Positional Encoding The model learns positional vectors during training Disadvantages Training time increases Cannot handle sequences longer than training length Sinusoidal Positional Encoding Uses mathematical functions like sine and cosine Even index → Sin Odd index → Cos These functions generate unique patterns for each position Advantages Can generalize to longer sequences No need to learn during training Efficient and stable This is the method used in the original Transformer paper. Why Positional En