Notes:
Recurrent neural networks (RNNs) are a type of artificial neural network that are designed to process sequential data. They are called “recurrent” because they make use of feedback connections, allowing information to be passed from one step in the sequence to the next. This makes them well-suited for tasks that involve processing sequences of data, such as natural language processing, speech recognition, and time series analysis.
RNNs are often used in dialog systems to understand and generate text. They can be trained on large datasets of text to learn the patterns and structure of language, and then used to generate responses to input text. RNNs can also be used to classify text, such as identifying the sentiment of a piece of text or determining the topic of a conversation.
RNNs have several advantages over traditional neural networks, including the ability to process sequential data and the ability to retain information from previous time steps. However, they can also be more difficult to train and may require more computing resources.
Resources:
- numpy.org .. python library supporting large, multi-dimensional arrays and matrices
See also:
RNN (Recurrent Neural Network) & Chatbots
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