RNNs Flashcards
What is a Recurrent Neural Network?
RNNs are a type of neural network designed to process sequential data by maintaining a hidden state that acts as memory of past inputs. This makes them suitable for tasks involving time-series data
Why do RNNs need memory?
The sources do not directly address this, but it can be inferred that RNNs require memory to understand the context in sequential data, where the next data point depends on previous data points. For example, in sentences, the meaning of a word often depends on the words before it.
What kind of data is often used with RNNs?
RNNs are often used with time series data, which is a sequence of data points indexed in time. Examples include stock prices, weather data, and words in a sentence
What is an LSTM network?
Long Short-Term Memory (LSTM) networks are a type of RNN that use gates to control the flow of information in and out of memory cells, helping them learn long-range dependencies in the data. An LSTM cell has 4 inputs and 1 outpu
What are the gates in an LSTM network?
What are the gates in an LSTM network?
What is a GRU network?
A Gated Recurrent Unit (GRU) network is a variant of LSTM with fewer parameters, also designed to capture long-range dependencies. It combines the forget and input gates into a single “update” gate.
What is Backpropagation Through Time (BPTT)?
BPTT is not directly mentioned in the provided sources. However, it can be inferred that BPTT is a learning algorithm used to train RNNs. It works by “unrolling” the network over time and applying backpropagation to calculate the gradients.
What are some common applications for RNNs?
RNNs are used in various applications, including time series prediction, natural language processing tasks such as image captioning, and speech recognition.
