Understanding sequential data processing – from traditional neural networks to RNNs and LSTMs

In traditional neural networks, as we discussed earlier in this book, we see an arrangement of densely interconnected neurons, devoid of any form of memory. When we feed a sequence of data to these networks, it’s an all-or-nothing transaction – the entire sequence is processed at once and converted into a singular vector representation. This approach is quite different from how humans process and comprehend text data. When we read, we naturally analyze text word by word, understanding that important words – those that have the power to shift the entire message of a sentence – can be positioned anywhere within it. For example, let's consider the sentence “I loved the movie, despite some critics.” Here, the word “despite” is pivotal, altering the direction of the sentiment expressed in the sentence.

RNNs don’t just...