ch. 8: Artificial neural networks Introduction to Back Propagation Neural Networks bpnn By KH Wong Neural Networks Ch9
Ch. 8: Artificial Neural networks Introduction to Back Propagation Neural Networks BPNN By KH Wong Neural Networks Ch9. , ver. 9b 1
Introduction Neural Network research is are very hot a high performance classifier(multi-class Successful in handwritten optical character OCR recognition speech recognition image Random Sampling of MNIST noise removal etc ° Easy to implement 图四DB Slow in learning 085 Fast in classification Example and dataset http://yann.lecun.com/exdb/mnist/ Neural Networks Ch9
Introduction • Neural Network research is are very hot • A high performance Classifier (multi-class) • Successful in handwritten optical character OCR recognition, speech recognition, image noise removal etc. • Easy to implement – Slow in learning – Fast in classification Neural Networks Ch9. , ver. 9b 2 Example and dataset: http://yann.lecun.com/exdb/mnist/
Motivation Biological findings inspire the development of Neural net -nput→ weights >Logic function→ output Neuron(Logic function) Biological relation Input Dendrites INputs Output W=weights Human computes using a net Output https://www.ninds.nihgov/disorders/patient-caregiver-education/life-and-death-neuron Neural Networks Ch9. ver. 9b
Motivation • Biological findings inspire the development of Neural Net – Input →weights →Logic function→ output • Biological relation – Input – Dendrites – Output – Human computes using a net Neural Networks Ch9. , ver. 9b 3 X=inputs W=weights Neuron(Logic function) Output https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Life-and-Death-Neuron
Applications 家例,个人都有 人工爱机题人, ,区一开来馆 Microsoft xiaolce. Al 我理划二代人,这次 化,出有见新冠 部的日,考一下受的,在 httpiimage SVRC 20 200 net. org/challenges/LSVRC/2015, Number of object cla )机号码、作周于们 作考一一证在 Num images 养一个,占出 传,人的容机 200 categories: accordion Training Num objects airplane, ant antelope Num images 20121 dishwasher dog domestic Validatio Num objects 55502 cat dragonfly, drum dumbbell Num images 40152 , etc. Testing Num objects · Tensor flov person motorcycle 侧画湿 Car 崮一 erson Neural IycLwuIna LI. vEl, Ju
Applications • Microsoft: XiaoIce. AI • http://imagenet.org/challenges/LSVRC/2015/ – 200 categories: accordion, airplane ,ant ,antelope ….dishwasher ,dog ,domestic cat ,dragonfly ,drum ,dumbbell , etc. • Tensor flow Neural Networks Ch9. , ver. 9b 4 ILSVRC 2015 Number of object classes 200 Training Num images 456567 Num objects 478807 Validation Num images 20121 Num objects 55502 Testing Num images 40152 Num objects ---
Different types of artificial neural networks Autoencoder DNN Deep neural network & Deep learning MLP Multilayer perceptron RNN(Recurrent Neural Networks), LSTM(Long Short-term memory) RBM Restricted boltzmann machine SOM Self-organizing map Convolutional neural network cnn Fromhttps://en.wikipedia.org/wiki/artificial_neuRal_network The method discussed in this power point can be applied to many of the above n Neural Networks Ch9
Different types of artificial neural networks • Autoencoder • DNN Deep neural network & Deep learning • MLP Multilayer perceptron • RNN (Recurrent Neural Networks), LSTM (Long Short-term memory) • RBM Restricted Boltzmann machine • SOM (Self-organizing map) • Convolutional neural network CNN • From https://en.wikipedia.org/wiki/Artificial_neural_network • The method discussed in this power point can be applied to many of the above nets. Neural Networks Ch9. , ver. 9b 5