- What are sequential problems in machine learning?
Sequential problems are a class of problem in machine learning in which the order of the features presented to the model is important for making predictions. Examples of sequential problems include NLP problems (for example, speech and text) and time series problems.
- What are some reasons that make it challenging for AI to solve sentiment analysis problems?
Human languages often contain words that have different meanings, depending on the context. It is therefore important for a machine learning model to fully understand the context before making a prediction. Furthermore, sarcasm is common in human languages, which is difficult for an AI-based model to comprehend.
- How is an RNN different than a CNN?
RNNs can be thought of as multiple, recursive copies of a single neural network. Each layer in an RNN passes its...
