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Deep Learning Mini Series: What are Recurrent Neural Networks (RNNs)?

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Manage episode 424766417 series 3578824
Content provided by Emily Laird. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Emily Laird or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of our deep learning mini-series, we explore Recurrent Neural Networks (RNNs). Imagine reading a mystery novel, keeping track of all the clues and characters—RNNs are like your super-intelligent reading buddy, remembering past events to make sense of the present. Perfect for processing sequences of data like text and speech, RNNs are valuable where context matters. We’ll explore their key components, such as recurrent layers and hidden states, and see real-world applications from language translation to financial forecasting.
Connect with Emily Laird on LinkedIn

  continue reading

18 episodes

Artwork
iconShare
 
Manage episode 424766417 series 3578824
Content provided by Emily Laird. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Emily Laird or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of our deep learning mini-series, we explore Recurrent Neural Networks (RNNs). Imagine reading a mystery novel, keeping track of all the clues and characters—RNNs are like your super-intelligent reading buddy, remembering past events to make sense of the present. Perfect for processing sequences of data like text and speech, RNNs are valuable where context matters. We’ll explore their key components, such as recurrent layers and hidden states, and see real-world applications from language translation to financial forecasting.
Connect with Emily Laird on LinkedIn

  continue reading

18 episodes

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