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In the last video, we discussed bi-directional RNNs. Today, we will focus on converting words into numbers for natural language processing tasks. Machine learning models require text input to be converted into numbers for understanding. For example, in a cricket NLP model, entities like player names, team names, and tournaments need to be recognized. This process is known as named entity recognition. However, the challenge lies in converting text input, like player names, into numbers that the machine can comprehend accurately.
