What are Recurrent Neural Networks in Deep Learning?

Recurrent Neural Networks (RNN)

RNNs retain data from previous inputs to make predictions or decisions. With a loop inside their structure, they pass data from one step to another, creating a form of memory

Structure of an RNN

RNNs' ability to retain memory & process data is fueled by the hidden state that enables these networks to capture complex patterns

Image & Speech Recognition

RNNs can analyze sequences of images or audio to identify objects, recognize speech, & even generate captions for images

Vanishing & Exploding Gradients

Vanishing & exploding gradients block RNNs' ability to retain data over long sequences. This is solved by  Long Short-Term Memory Networks (LTSM).

Long Short-Term Memory Networks 

LTSMs use a memory cell &  gates. The memory cell stores relevant data over long sequences. The gates control the flow of information in the memory cell

Paving the Way for the Future  

RNNs will remain vital in the future while helping machines understand data & enabling applications like language translation & predictive modeling

Embrace Innovations with RNNs!