Transformers, are type of neural networks that has gained popularity, was introduced in the paper ‘Attention is all you need’ by Google in 2017. The Transformer architecture relies entirely on the attention mechanism drawing global dependencies between input and output. The Transformer allows for significantly more parallelization. Why Transformers? In…

Sequence to to sequence models have been used quite successfully in a number of machine learning tasks. When the topic of sequence to sequence model comes up, the first neural network model that comes up in our mind are the Recurrent Neural Networks configured in the Encoder-Decoder format. Convolutional neural…

Recurrent Neural Networks Recurrent Neural Networks (RNN) are models which are popularly used for sequence processing tasks and works well on sequential data. RNNs has shown a great success in solving Natural Language processing (NLP) problems. RNNs are a generalization of feed forward neural network with an internal memory. They are ‘recurrent’ because…