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Deep learning - Conceptual understanding and applications

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Buhwan Jeong
Buhwan Jeong

딥러닝 Deep Learning에 관해서 팀에 공유했던 발표자료입니다.

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Deep Learning Conceptual Understanding & Application Variants ben.hur@daumkakao.com The aim of this slide is at conceptual understanding of deep learning, rather than implementing it for practical applications. That is, this is more for paper readers, less for system developers. Note that, some variables are not uniquely defined nor consistently used for convenience. Also, variable/constant and vector/matrix are not clearly distinct. More importantly, some descriptions may be incorrect due to lack of my ability.
Deep Learning (DL) is, in concise, a deep and wide neural network. That is half-true.
Deep Learning is difficult because of lack of understanding about — artificial neural network (ANN) — unfamiliar applications.
DL — ANN — Signal transmission between neurons — Graphical model (Belief network) — Linear regression / logistic regression — Weight, bias & activation — (Feed-forward) Multi-layer perceptron (MLP) — Optimization — Back-propagation — (Stochastic) Gradient-decent
DL — Applications — Speech recognition (Audio) — Natural language processing (Text) — Information retrieval (Text) — Image recognition (Vision) — & yours (+)
Artificial Neural Network (ANN)
ANN is a nested ensemble of [logistic] regressions.
Perceptron & Message Passing
http://en.wikipedia.org/wiki/Neuron
5 5 y = x Message passing
y = Σ x y = Σ wx y = Σ wx + b Linear regression
y = sigm(Σwx + b) Logistic regression Σwx?Σwx
Activation function — Linear g(a) = a — Sigmoid g(a) = sigm(a) = 1 / (1 + exp(-a)) — Tanh g(a) = tanh(a) = (exp(a) - exp(-a)) / (exp(a) + exp(-a)) — Rectified linear g(a) = reclin(a) = max(0, a) — Step — Gaussian — Softmax, usually for the output layer
Bias controls activation. y = ax b y = ax + b
Multi-Layer Perceptron (MLP)
Linearly non-separable by a single perceptron
Input layer Hidden layer Output layer http://info.usherbrooke.ca/hlarochelle/ neural_networks/content.html Interconnecting weight Bias Activation function
http://info.usherbrooke.ca/hlarochelle/ neural_networks/content.html
Back-Propagation & Optimization
{x, y} h=a(wx+b) y'=a2(w2h+b2) l = y - y'w2 & b2w & b w2 R & b2 RwR & bR y' Back-propagation {x, y} —> feed-forward <— back-propagation
Reinforced through epoch ANN is a reinforcement learning.
Local minimum Plateaus http://info.usherbrooke.ca/hlarochelle/ neural_networks/content.html
Stochastic Gradient Descent The slide excludes details. See reference tutorial for that.
ANN is a good approximator of any functions, if well-designed and trained.
Deep Learning
DL (DNN) is a deep and wide neural network. many (2+) hidden layers many input/hidden nodes
DEEP WIDE Large matrix Many matrix Image from googling
Brain image from Simon Thorpe Other from googling
Many large connection matrices (W) — Computational burden (GPU) — Insufficient labeled data (Big Data) — Under-fitting (Optimization) — Over-fitting (Regularization)
Unsupervised makes DL success.
Unsupervised pre-training — Initialize W matrices in an unsupervised manner — Restricted Boltzmann Machine (RBM) — Auto-encoder — Pre-train in a layer-wise (stacking) fashion — Fine-tune in a supervised manner
Unsupervised representation learning — Construct a feature space — DBM / DBN — Auto-encoder — Sparse coding — Feed new features to a shallow ANN as input cf, PCA + regression
visible hidden Restricted Boltzmann Machine (RBM)
Boltzmann Machine Example
visible hidden
Auto-encoder x x' W WT encoder decoder
if k <= n (single & linear), then AE becomes PCA. otherwise, AE can be arbitrary (over-complete). Hidden & Output layers plays the role of semantic feature space in general - compresses & latent feature (under-complete) - expanded & primitive feature (over-complete)
Denoising auto-encoder x x' nois(x)
Contractive auto-encoder Regularized auto-encoder Same as Ridge (or similar to Lasso) Regression
Hinton and Salakhutdinov, Science, 2006 Deep auto-encoder
(k > n, over-complete) Sparse coding
X Y
RBM3 RBM2 RBM1 Stacking Training in the order of RBM1, RBM2, RBM3, …
Representation Learning Artificial Neural Network+ - Sparse coding - Auto-encoder - DBN / DBM
Stochastic Dropout Training for preventing over-fitting
http://info.usherbrooke.ca/hlarochelle/ neural_networks/content.html
Deep Architecture
Deep architecture is an ensemble of shallow networks.
Key Deep Architectures — Deep Neural Network (DNN) — Deep Belief Network (DBN) — Deep Boltzmann Machine (DBM) — Recurrent Neural Network (RNN) — Convolution Neural Network (CNN) — Multi-modal/multi-tasking — Deep Stacking Network (DSN)
Applications
Applications — Speech recognition — Natural language processing — Image recognition — Information retrieval Application Characteristic - Non-numeric data - Large dimension (curse of dimensionality)
Natural language processing — Word embedding — Language modeling — Machine translation — Part-of-speech (POS) tagging — Named entity recognition — Sentiment analysis — Paraphrase detection — …
Deep-structured semantic modeling (DSSM) BM25(Q, D) vs Corr(sim(Q, D), CTR)
DBN & DBM
Recurrent Neural Network (RNN)
Recurrent Neural Network (RNN)
http://nlp.stanford.edu/courses/NAACL2013/ NAACL2013-Socher-Manning-DeepLearning.pdf Recursive Neural Network
Convolution Neural Network (CNN)
C-DSSM
CBOW & Skip-gram (Word2Vec)
Deep Stacking Network (DSN)
Tensor Deep Stacking Network (TDSN)
Multi-modal / Mulit-tasking
Google Picture to Text
Content-based Spotify Recommendation
In various applications, Deep Learning provides either — better performance than state-of-the-art — similar performance with less labor
Remarks
Deep Personalization with Rich Profile in a Vector Representation - Versatile / flexible input - Dimension reduction - Same feature space ==> Similarity computation
RNN for CF http://erikbern.com/?p=589
Hard work — Application-specific configuration — Network structure — Objective and loss function — Parameter optimization
Yann LeCun —> Facebook Geoffrey Hinton —> Google Andrew Ng —> Baidu Joshua Benjio —> U of Montreal
References Tutorial — http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html — http://deeplearning.stanford.edu/tutorial Paper — Deep learning: Method and Applications (Deng & Yu, 2013) — Representation learning: A review and new perspective (Benjio et al., 2014) — Learning deep architecture for AI (Benjio, 2009) Slide & Video — http://www.cs.toronto.edu/~fleet/courses/cifarSchool09/slidesBengio.pdf — http://nlp.stanford.edu/courses/NAACL2013 Book — Deep learning (Benjio et al. 2015) http://www.iro.umontreal.ca/~bengioy/dlbook/
Open Sources (from wikipedia) — Torch (Lua) https://github.com/torch/torch7 — Theano (Python) https://github.com/Theano/Theano/ — Deeplearning4j (word2vec for Java) https://github.com/SkymindIO/deeplearning4j — ND4J (Java) http://nd4j.org https://github.com/SkymindIO/nd4j — DeepLearn Toolbox (matlab) https://github.com/rasmusbergpalm/DeepLearnToolbox/ graphs/contributors — convnetjs (javascript) https://github.com/karpathy/convnetjs — Gensim (word2vec for Python) https://github.com/piskvorky/gensim — Caffe (image) http://caffe.berkeleyvision.org — More+++
FBI WARNING Some reference citations are missed. Many of them come from the first tutorial and the first paper, and others from googling. All rights of those images captured, albeit not explicitly cited, are reserved to original authors. Free to share, but cautious of that.
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