Transformers, the better of neural networks?

The transformer is a class of neural networks designed to process sequential data. As the name suggests, it ‘transforms’ input sequences into output sequences. This includes, but is not limited to speech recognition, language translation, and text-to-speech.

How do transformers work?

Transformers use something known as the Attention mechanism, specifically self-attention. This has been a relatively recent development to that used in RNNs, LSTMs, etc. To explain the technique, let’s take into consideration the case of language translation. We are aware of the fact that a word-by-word translation from one language to another almost always results in something nonsensical. Differences in sentence structuring, grammar, etc between the languages contribute to this issue. So, the technique assumes that there is meaning and relevant information attributed to every word in the text.

A transformer comprises six encoders and six decoders. The encoder further comprises two layers: Self-attention and Feed Forward Neural Network.

In the encoder, the input first flows through a self-attention layer. It helps the encoder look at other words while encoding the input word. The decoder meanwhile has an extra attention layer besides the two layers. This aids the decoder in focusing on relevant parts of the input sentence.

In a language model, nearby words would first get grouped. As mentioned, the transformer processes input so that every word in the input data connects logically. As soon as the transformer starts its training it can oversee pieces of the complete dataset.

How are transformers superior?

The transformer combines the best features of recurrent neural networks and convolutional neural networks.

• It understands the relationship between sequential elements that are far from each other.
• Higher accuracy.
• It processes more data in lesser time. In other words, transformers are much faster than their counterparts.
• Transformers can work with almost all kinds of sequential data.

What are the Industrial applications of Transformers?

Genes, proteins in a molecule, code, playlists, and online behaviors such as browsing or likes, or purchases are all examples of sequential data that can be processed by transformers. Transformers have the potential to be used in anomaly detection from fraud detection to system health monitoring and process manufacturing operations.

Conclusion

Transformers though a novel technology in AI, is quite promising. These replacing traditional neural networks seem to be imminent soon. If anything, at the least acquiring knowledge of transformers, will help solidify your understanding of neural networks.