PPT-Ch. 9: Introduction to Convolution Neural Networks

CNN KH Wong CNN V7b 1 Introduction Very Popular Toolboxes tensorflow cudaconvnet and caffe user friendlier A high performance Classifier multiclass Successful in object recognition handwritten optical character OCR recognition image noise removal etc. Bullinaria 2004 1 Introduction to Radial Basis Functions 2 Exact Interpolation 3 Common Radial Basis Functions 4 Radial Basis Function RBF Networks 5 Problems with Exact Interpolation Networks 6 Improving RBF Networks 7 The Improved RBF Network brPa Easy to understand Easy to code by hand Often used to represent inputs to a net Easy to learn This is what mixture models do Each cluster corresponds to one neuron Easy to associate with other representations or responses But localist models are ver Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. Honglak. Lee, Roger Grosse, Rajesh . Ranganath. , Andrew Y. Ng. Playing Atari with Deep Reinforcement Learning. . Linear classifiers on pixels are bad. Solution 1: Better feature vectors. Solution 2: Non-linear classifiers. A pipeline for recognition. Compute image gradients. Compute SIFT descriptors. Assign to k-means centers. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. Introduction to Back Propagation Neural . Networks BPNN. By KH Wong. Neural Networks Ch9. , ver. 8d. 1. Introduction. Neural Network research is are very . hot. . A high performance Classifier (multi-class). Dr. Abdul Basit. Lecture No. 1. Course . Contents. Introduction and Review. Learning Processes. Single & Multi-layer . Perceptrons. Radial Basis Function Networks. Support Vector and Committee Machines. Shuo. Yu. October 3, 2018. 1. 10/11/2018. Acknowledgments. Many of the images, results, and other materials are from:. Deep . Learning, . Ian . Goodfellow. , . Yoshua. . Bengio. , and Aaron . Courville. Dr David Wong. (With thanks to Dr Gari Clifford, G.I.T). The Multi-Layer Perceptron. single layer can only deal with linearly separable data. Composed of many connected neurons . Three general layers; . An overview and applications. Outline. Overview of Convolutional Neural Networks. The Convolution operation. A typical CNN model architecture. Properties of CNN models. Applications of CNN models. Notable CNN models.