This episode breaks down the 'RECURRENT NEURAL NETWORK REGULARIZATION' research paper, which investigates how to correctly apply a regularization technique called dropout to Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. The authors argue that dropout, while effective in traditional neural networks, has limitations in RNNs. They propose a modified implementation of dropout specifically for RNNs and LSTMs, which significantly reduces overfitting across various tasks such as language modelling, speech recognition, machine translation, and image caption generation. The paper provides a detailed explanation of the proposed technique, its effectiveness through experimental results, and comparisons with existing approaches.Audio : (Spotify) https://open.spotify.com/episode/51KtuybPXYBNu7sfVPWFZK?si=T_GBETMHTAK8rFOZ_lr4oQPaper: https://arxiv.org/abs/1409.2329v5

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