ch,9: Introduction to convolution Neural Networks (CNN) and systems KH Wong
Ch. 9: Introduction to Convolution Neural Networks (CNN) and systems KH Wong ch9. CNN. V9b3 1
Overview Part 1 Al. theory of cnN A2 Feed forward details A2 Back propagation details Part B: CNN Systems Part c: cnn tools ch9. CNN. V9b3
Overview • Part 1 – A1. Theory of CNN – A2. Feed forward details – A2. Back propagation details • Part B: CNN Systems • Part C: CNN Tools ch9. CNN. V9b3 2
Introduction Very popular Toolboxes: tensorflow, cuda-convnet and caffe( user friendlier) a high performance Classifier(multi-class Successful in object recognition, handwritten optical character oCr recognition, image noise removal etc Easy to implementation Slow in learning Fast in classification ch9. CNN. V9b3
Introduction • Very Popular: – Toolboxes: tensorflow, cuda-convnet and caffe (user friendlier) • A high performance Classifier (multi-class) • Successful in object recognition, handwritten optical character OCR recognition, image noise removal etc. • Easy to implementation – Slow in learning – Fast in classification ch9. CNN. V9b3 3
Overview of this note Prerequisite: knowledge of fully connected Back Propagation Neural Networks (BPNN), in http://www.cse.cuhk.edu.hk//khwong/www2/cm sc5707 5707 08 neural net. pptx Convolution neural networks(Cnn) -Part a2 feed forward of cnn Part a3: feed backward of cnn ch9. CNN. V9b3
Overview of this note • Prerequisite: knowledge of Fully connected Back Propagation Neural Networks (BPNN), in – http://www.cse.cuhk.edu.hk/~khwong/www2/cm sc5707/5707_08_neural_net.pptx • Convolution neural networks (CNN) – Part A2: feed forward of CNN – Part A3: feed backward of CNN ch9. CNN. V9b3 4
Part A1 Theory of cnn Convolution Neural Networks
Part A.1 Theory of CNN Convolution Neural Networks ch9. CNN. V9b3 5