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Recurrent Neural Network tutorial (2nd)

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ujava.org workshop : Recurrent Neural Network , RNN tutorial , LSTM, GRU, BPTT

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Recurrent Neural Network (2nd) ujava.org workshop 2016-10-03 www.idosi.com CEO 강신동 Shindong KANG (주)지능도시
www.idosi.comujava.org
www.idosi.comspaceapi.org
www.idosi.comRecurrent Stream (순환류)
www.idosi.comRecurrent Iteration
www.idosi.comConvergence & Attractor (수렴 & 끌개)
www.idosi.comDivergence (발산)
www.idosi.comCobweb (거미줄망)
www.idosi.comCobweb Plot (Cobweb Diagram)
www.idosi.comElman Recurrent Neural Network
www.idosi.comJordan Recurrent Neural Network
www.idosi.comSequence data
www.idosi.comRNN
www.idosi.comRNN (hidden state, h)
www.idosi.comRNN
www.idosi.comRNN
www.idosi.comVanilla word Vanilla Common Euphemism (완곡어구) for “regular” or “without any fancy stuff” JFYI: fancy stuff (장식 재료)
www.idosi.comVanilla RNN
www.idosi.comOne-hot encoding
www.idosi.comSoftmax
www.idosi.comLighten Softmax
www.idosi.comCharacter Level Language Model
www.idosi.comInput Vector
www.idosi.comCLLM Vanilla RNN
www.idosi.comCLLM Vanilla RNN
www.idosi.comCLLM Vanilla RNN
www.idosi.comRNN
www.idosi.comOne to one
www.idosi.comOne to many (image captioning)
www.idosi.comMany to one (sentiment, 감정 분류)
www.idosi.comMany to many
www.idosi.comMany to many (video captioning)
www.idosi.comDeep Learning for Music Composition
www.idosi.comRNN hand writing recognition
www.idosi.comcaptioning
www.idosi.comMulti-Layer RNN
www.idosi.comBPTT (Back-Propagation Through Time)
www.idosi.comDeep Gradient by gradients multiply
www.idosi.comExploding Gradient & Vanishing Gradient
www.idosi.comExploding Gradients : Gradient Clipping
www.idosi.comVanishing Gradients : memory loss
www.idosi.comRNN Weights
www.idosi.comError of BPTT
www.idosi.comCost function of BPTT
www.idosi.comOuter Product of vector
www.idosi.comBPTT of V
www.idosi.comBPTT of W
www.idosi.comBPTT of W
www.idosi.comBPTT sum
www.idosi.comBidirectional RNN
www.idosi.comDeep Bidirectional RNN
www.idosi.comSigmoid function
www.idosi.comDerivative of sigmoid function
www.idosi.comHyperbolic tangent function
www.idosi.comLSTM & GRU computing target
www.idosi.comLSTM concept
www.idosi.comLSTM (Long-Short Term Memory) Sc = cell state h = activation function Yc = cell output
www.idosi.comLSTM (Long-Short Term Memory)
www.idosi.comLSTM (Long-Short Term Memory)
www.idosi.comLSTM equations
www.idosi.comLSTM to simple RNN
www.idosi.comLSTM with step delay n-th layer k-th cell 1 : step delay Learn if input gate and output gate ON
www.idosi.comGRU concept
www.idosi.comGRU concept r : reset gate , 1 = input level z : update gate , 0 = fully new update
www.idosi.comGRU (Gated Recurrent Unit, Dr Cho)
www.idosi.comGRU equations
www.idosi.comLSTM GRU RNN activation function No Good Good
www.idosi.comLSTM & GRU
www.idosi.comLSTM & GRU multilayer
www.idosi.com Thank you ! (주)지능도시 Intelligent City Ltd. 강신동 Shindong KANG www.idosi.com ceo@idosi.com

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