Why transformers are better for NLP ? Let’s see the math behind it – Day 64

  Understanding RNNs & Transformers in Detail: Predicting the Next Letter in a Sequence We have been focusing on NLP on today article and our other two articles of  Natural Language Processing (NLP) -RNN – Day 63 &  The Revolution of Transformer Models – day 65. In this article explanation, we’ll delve deeply into how Recurrent Neural Networks (RNNs) and Transformers work, especially in the context of predicting the next letter “D” in the sequence “A B C”. We’ll walk through every step, including actual numerical calculations for a simple example, to make the concepts clear. We’ll also explain why Transformers are considered as neural networks and how they fit into the broader context of deep learning.  Recurrent Neural Networks (RNNs) Introduction to RNNs RNNs are a type of neural network designed to process sequential data by maintaining a hidden state that captures information about previous inputs. This makes them suitable for tasks like language modeling, where the context provided by earlier letters influences the prediction of the next letter. Problem Statement Lets say, Given the sequence of “A B C”, we want the RNN to predict the next letter, which is “D”. Input Representation We need to represent each…