recurrent neural network tutorial (2nd)

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Recurrent Neural Network (2nd)

ujava.org workshop

2016-10-03

www.idosi.com

CEO Shindong KANG

()

ujava.org

spaceapi.org

Recurrent Stream ()

Recurrent Iteration

Convergence & Attractor ( & )

Divergence ()

Cobweb ()

Cobweb Plot (Cobweb Diagram)

Elman Recurrent Neural Network

Jordan Recurrent Neural Network

Sequence data

RNN

RNN (hidden state, h)

RNN

RNN

Vanilla word

Vanilla

Common Euphemism ()

for regular or without any fancy stuff

JFYI: fancy stuff ( )

Vanilla RNN

One-hot encoding

Softmax

Lighten Softmax

Character Level Language Model

Input Vector

CLLM Vanilla RNN

CLLM Vanilla RNN

CLLM Vanilla RNN

RNN

One to one

One to many (image captioning)

Many to one (sentiment, )

Many to many

Many to many (video captioning)

Deep Learning for Music Composition

RNN hand writing recognition

captioning

Multi-Layer RNN

BPTT (Back-Propagation Through Time)

Deep Gradient by gradients multiply

Exploding Gradient & Vanishing Gradient

Exploding Gradients : Gradient Clipping

Vanishing Gradients : memory loss

RNN Weights

Error of BPTT

Cost function of BPTT

Outer Product of vector

BPTT of V

BPTT of W

BPTT of W

BPTT sum

Bidirectional RNN

Deep Bidirectional RNN

Sigmoid function

Derivative of sigmoid function

Hyperbolic tangent function

LSTM & GRU computing target

LSTM concept

LSTM (Long-Short Term Memory)

Sc = cell state h = activation functionYc = cell output

LSTM (Long-Short Term Memory)

LSTM (Long-Short Term Memory)

LSTM equations

LSTM to simple RNN

LSTM with step delay

n-th layerk-th cell1 : step delay

Learn if input gate and output gate ON

GRU concept

GRU concept

r : reset gate , 1 = input level z : update gate , 0 = fully new update

GRU (Gated Recurrent Unit, Dr Cho)

GRU equations

LSTM GRU RNN activation function

No Good

Good

LSTM & GRU

LSTM & GRU multilayer

Thank you !

()Intelligent City Ltd.

Shindong KANG

www.idosi.comceo@idosi.com

www.idosi.com .

www.idosi.com .

www.idosi.com .

www.idosi.com .

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