APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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It can be trained as a supervised learning problem.
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It is strictly more powerful than a Convolutional Neural Network (CNN).
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It is applicable when the input/output is a sequence (e.g., a sequence of words).
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RNNs represent the recurrent process of Idea
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Detailed explanation-1: -Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? It can be trained as a supervised learning problem. It is strictly more powerful than a Convolutional Neural Network (CNN).
Detailed explanation-2: -A recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data’s sequential characteristics and use patterns to predict the next likely scenario.
Detailed explanation-3: -Because of their internal memory, RNNs can remember important things about the input they received, which allows them to be very precise in predicting what’s coming next. This is why they’re the preferred algorithm for sequential data like time series, speech, text, financial data, audio, video, weather and much more.
Detailed explanation-4: -The task of machine translation consists of reading text in one language and generating text in another language. When neural networks are used for this task, we talk about neural machine translation (NMT)[i] [ii]. Within NMT, the encoder-decoder structure is quite a popular RNN architecture.