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Deep Learning A-Z™: Recurrent Neural Networks (RNN) - Module 3

K
Kirill Eremenko

This document provides an overview of recurrent neural networks and long short-term memory (LSTM) networks. It discusses the vanishing gradient problem with traditional RNNs and how LSTMs address this issue using gating mechanisms. Several LSTM variations and practical intuition on LSTMs are also covered.

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Related topics:
Deep Learning • Neural Networks
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© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z What we will learn in this section: • The idea behind Recurrent Neural Networks • The Vanishing Gradient Problem • Long Short-Term Memory (LSTM) • Practical Intuition • EXTRA: LSTM Variations
© SuperDataScienceDeep Learning A-Z Used for Regression & ClassificationArtificial Neural Networks Used for Computer VisionConvolutional Neural Networks Used for Time Series AnalysisRecurrent Neural Networks Used for Feature DetectionSelf-Organizing Maps Used for Recommendation SystemsDeep Boltzmann Machines Used for Recommendation SystemsAutoEncoders SupervisedUnsupervised
© SuperDataScienceDeep Learning A-Z Image Source: Wikipedia Cerebellum Brainstem Cerebrum Frontal Lobe Temporal Lobe Parietal Lobe Occipital Lobe
© SuperDataScienceDeep Learning A-Z Input value 1 Input value 2 Input value 3 Output values Input Layer Hidden Layer Output Layer 4 5 6 7 5 6 2 1 1
© SuperDataScienceDeep Learning A-Z 456 Input value 1 Input value 2 Input value 3 Output values Input Layer Hidden Layer Output Layer 7 56211
© SuperDataScienceDeep Learning A-Z 456Input Vector Output Vector Input Layer Hidden Layer Output Layer 7 56211
© SuperDataScienceDeep Learning A-Z Input Vector Output Vector Input Layer Hidden Layer Output Layer 7 51
© SuperDataScienceDeep Learning A-Z 751
© SuperDataScienceDeep Learning A-Z 517
© SuperDataScienceDeep Learning A-Z 517 Time
© SuperDataScienceDeep Learning A-Z 517 Time 517 517 517
© SuperDataScienceDeep Learning A-Z 517 Time 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517 517
© SuperDataScienceDeep Learning A-Z 517 Time 517 517 517
© SuperDataScienceDeep Learning A-Z Time Many to Many Time One to Many Time Many to One Time Many to Many Reference: karpathy.github.io karpathy.github.io dev.havenondemand.com
© SuperDataScienceDeep Learning A-Z Sunspring (movie, 2016) • Directed By Oscar Sharp • Written by Benjamin Link: https://arstechnica.com/the-multiverse/2016/06/an-ai-wrote-this-movie-and- its-strangely-moving/ Additional Reading:
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Image Source: people.idsia.ch
© SuperDataScienceDeep Learning A-Z Image Source: Université Montréal
© SuperDataScienceDeep Learning A-Z Image Source: recode.net
© SuperDataScienceDeep Learning A-Z C ŷ C = ½(ŷ- y)2
© SuperDataScienceDeep Learning A-Z y X2 X1 W11,1 W11,2 W11,3 W12,1 W12,2 W12,3 W21,1 W22,1 W23,1
© SuperDataScienceDeep Learning A-Z 517 Time 517 517 517 517 Win Wout Wrec Win Wout Wrec Win Wout Wrec Win Wout Wrec Win Wout yt εt xtxt-1xt-2xt-3 Formula Source: Razvan Pascanu et al. (2013) εt+1εt-1εt-2εt-3 Wrec ~ small Wrec ~ large Vanishing Exploding Wrec Wrec Wrec
© SuperDataScienceDeep Learning A-Z Solutions: 1. Exploding Gradient • Weight Initialization • Echo State Networks • Long Short-Term Memory Networks (LSTMs) • Truncated Backpropagation • Penalties • Gradient Clipping 2. Vanishing Gradient
© SuperDataScienceDeep Learning A-Z Untersuchungen zu dynamischen neuronalen Netzen By Sepp (Josef) Hochreiter (1991) Link: http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidh uber.pdf Additional Reading:
© SuperDataScienceDeep Learning A-Z Learning Long-Term Dependencies with Gradient Descent is Difficult By Yoshua Bengio et al. (1994) Link: http://www-dsi.ing.unifi.it/~paolo/ps/tnn-94-gradient.pdf Additional Reading:
© SuperDataScienceDeep Learning A-Z On the difficulty of training recurrent neural networks By Razvan Pascanu et al. (2013) Link: http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Additional Reading:
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Today: • A bit of history • LSTM Architecture • Example walkthrough
© SuperDataScienceDeep Learning A-Z 517 Time 517 517 517 517 Win Wout Wrec Win Wout Wrec Win Wout Wrec Win Wout Wrec Win Wout yt εt xtxt-1xt-2xt-3 Formula Source: Razvan Pascanu et al. (2013) εt+1εt-1εt-2εt-3 Wrec ~ small Wrec ~ large Vanishing Exploding Wrec Wrec Wrec Wrec = 1 < 1 > 1
© SuperDataScienceDeep Learning A-Z Image Sources: people.idsia.ch, ics.usi.ch
© SuperDataScienceDeep Learning A-Z 517 Time 517 517 517
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io
© SuperDataScienceDeep Learning A-Z Image Source: arxiv.org/pdf/1503.04069.pdf
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io
© SuperDataScienceDeep Learning A-Z Ct-1 Image Source: colah.github.io Ct
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Ct-1 Image Source: colah.github.io Ct
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Ct-1 Image Source: colah.github.io Ct
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io Ct-1 Ct
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io Ct-1 Ct
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io Ct-1 Ct
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io Ct-1 Ct
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z CtCt-1
© SuperDataScienceDeep Learning A-Z Long Short-Term Memory By Sepp Hochreiter & Jurgen Schmidhuber (1997) Link: http://www.bioinf.jku.at/publications/older/2604.pdf Additional Reading:
© SuperDataScienceDeep Learning A-Z Understanding LSTM Networks By Christopher Olah (2015) Link: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ Additional Reading:
© SuperDataScienceDeep Learning A-Z Understanding LSTM and its diagrams By Shi Yan (2016) Link: https://medium.com/@shiyan/understanding-lstm-and-its-diagrams- 37e2f46f1714 Additional Reading:
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io
© SuperDataScienceDeep Learning A-Z Image Source: karpathy.github.io
© SuperDataScienceDeep Learning A-Z Image Source: karpathy.github.io
© SuperDataScienceDeep Learning A-Z Image Source: karpathy.github.io
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io
© SuperDataScienceDeep Learning A-Z Image Source: karpathy.github.io
© SuperDataScienceDeep Learning A-Z The Unreasonable Effectiveness of Recurrent Neural Networks By Andrej Karpathy (2015) Link: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Additional Reading:
© SuperDataScienceDeep Learning A-Z Visualizing and Understanding Recurrent Networks By Andrej Karpathy et al. (2015) Link: https://arxiv.org/pdf/1506.02078.pdf Additional Reading:
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Ct-1 Image Source: colah.github.io Ct
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io Ct-1 Ct
© SuperDataScienceDeep Learning A-Z Image Source: colah.github.io Ct-1 Ct
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z LSTM: A Search Space Odyssey By Klaus Greff et al. (2015) Link: https://arxiv.org/pdf/1503.04069.pdf Additional Reading:

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Deep Learning A-Z™: Recurrent Neural Networks (RNN) - Module 3