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# Introductions to Neural Networks,Basic concepts

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### Introductions to Neural Networks,Basic concepts

1. 1. Introductions to Neural Networks Basic concepts Jian QIN Intro Single-Layer Introductions to Neural Networks Basic concepts Multilayer Applications Next Jian QIN December 23, 2013 . . . . . .
2. 2. Introductions to Neural Networks Basic concepts Outline Jian QIN Intro Intro Single-Layer Multilayer Applications Single-Layer Next Multilayer Applications Next . . . . . .
3. 3. Introductions to Neural Networks Basic concepts Show Jian QIN Intro Single-Layer Multilayer Applications Next ./asamples.gif Get more from http://yann.lecun.com/exdb/lenet/index.html . . . . . .
4. 4. Introductions to Neural Networks Basic concepts Show cont. Jian QIN Intro Single-Layer Multilayer Applications Next Source: Learning Internal Representations by Error Propagation . . . . . .
5. 5. Introductions to Neural Networks Basic concepts Neuron Jian QIN Intro Single-Layer Multilayer Applications Next ▶ Input Vector: X = [x0 = 1, x1 , x2 , x3 …] ▶ Weights: W = [w0 = b, w1 , w2 , w3 …] ▶ Sum: X*WT ▶ Activation Function … . . . . . .
6. 6. Introductions to Neural Networks Basic concepts Activation Function Jian QIN Intro Single-Layer ▶ Threshold Multilayer Applications 1 2 3 4 5 if < threshold then 0 else 1 ▶ Next Sigmoid 1 1. y = 1+e−ax 2. y = tanh(x) . . . . . .
7. 7. Introductions to Neural Networks Basic concepts Perceptrons Jian QIN Intro ▶ ▶ Single-Layer Can perform pattern classiﬁcation only on linearly separable patterns. Multilayer Applications XOR Problem Next a 1 0 1 0 b 1 1 0 0 a XOR b 0 1 1 0 Critique from Minsky and Selfridge . . . . . .
8. 8. Introductions to Neural Networks Basic concepts Perceptrons ▶ On-Line learning (One by One) Jian QIN Intro w(0) = [0, 0, 0...] Single-Layer w(n + 1) = w(n) + η [d(n) − y(n)] x(n) ▶ Multilayer Applications Batch Learning where E is the set of misclassifed x perceptron cost function J(w) = ∑ Next (−wT x) x∈E gradient vector J(w) = ∑ (−x) x∈E algorithm w(n + 1) = w(n) − η J(w) ∑ w(n + 1) = w(n) − η x . . x∈E . . . .
9. 9. Introductions to Neural Networks Basic concepts SVM Jian QIN A kernel learning method on (sort of) single layer perceptron. Intro Single-Layer Multilayer Applications Next From: PDF materials of Neural Networks and Learning Machines . . . . . .
10. 10. Introductions to Neural Networks Basic concepts Multilayer Perceptrons Jian QIN Intro ▶ Input + Hidden Layer + Output Single-Layer Multilayer Applications Next http://www.seattlerobotics.org/encoder/ nov98/neural.html . . . . . .
11. 11. Introductions to Neural Networks Basic concepts Feed Forward Jian QIN Intro Single-Layer Multilayer Applications Next xlayer(N+1) = sgn(xlayer(N) ∗ Wlayer(N) ) . . . . . .
12. 12. Introductions to Neural Networks Basic concepts Back-Propagation Jian QIN Intro Single-Layer Multilayer Cost Function Applications 1 ∑∑ 2 = ej (n) N N ξav Next n=1 j∈C ej (n) = dj (n) − yj (n) http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/ backprop.html . . . . . .
13. 13. Introductions to Neural Networks Basic concepts Word2Vec Jian QIN Intro Single-Layer Multilayer Applications Next Working on Java Version. 1/7 speed of original c version. Original Version: https://code.google.com/p/word2vec/ . . . . . .
14. 14. Introductions to Neural Networks Basic concepts matlab code Jian QIN Intro Single-Layer Multilayer Applications Next Hand-writing Image Processing . . . . . .
15. 15. Introductions to Neural Networks Basic concepts Personal Views Jian QIN Intro Single-Layer Multilayer Applications Next 1. Pattern Recognition: Image Processing. 2. Build Prior Information into NN: by hand. 3. Have no idea about the structure of the solution. . . . . . .
16. 16. Introductions to Neural Networks Basic concepts Next Jian QIN Intro Single-Layer Multilayer Applications Next 1. Recurrent NN 2. Applications 3. Neural Networks on NLP. . . . . . .
17. 17. Introductions to Neural Networks Basic concepts Links Jian QIN Intro Single-Layer Multilayer ▶ Machine Learning Applications Next https://www.coursera.org/course/ml ▶ Neural Networks for Machine Learning https://www.coursera.org/course/neuralnets ▶ Neural Networks and Learning Machines http://book.douban.com/subject/5952531/ . . . . . .