PPT-Learning Both Weights and Connections for Efficient Neural

Han et al Deep Compression Compressing Deep Neural Networks with Pruning Training Quantization and Huffman Coding Han et al Deep Compression Deep Learning on Embedded System Some Statistics. Week 7. 1. Team Homework Assignment #9. Read pp. 327 – 334 and the Week 7 slide.. Design a neural network for XOR (Exclusive OR). Explore neural network tools.. beginning of the lecture on Friday . What are Artificial Neural Networks (ANN)?. ". Colored. neural network" by Glosser.ca - Own work, Derivative of File:Artificial neural . network.svg. . Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg#/media/File:Colored_neural_network.svg. (sometimes called “Multilayer . Perceptrons. ” or MLPs). Linear . s. eparability. Feature 1. Feature 2. Hyperplane. In . 2D: . A perceptron can separate data that is linearly separable.. A bit of history. 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. Ashutosh. Pandey and . Shashank. . S. rikant. Layout of talk. Classification problem. Idea of gradient descent . Neural network architecture. Learning a function using neural network. Backpropagation algorithm. Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. Rohit. Ray. ESE 251. What are Artificial Neural Networks?. ANN are inspired by models of the biological nervous systems such as the brain. Novel structure by which to process information. Number of highly interconnected processing elements (neurons) working in unison to solve specific problems.. Practical Advice I. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Input Normalization. Reminder from past lecture. True whether . Goals for this Unit. Basic. understanding of Neural Networks and how they work. Ability to use Neural Networks to solve real problems. Understand when neural networks may be most appropriate. Understand the strengths and weaknesses of neural network models. CS295: Modern Systems: Application Case Study Neural Network Accelerator – 2 Sang-Woo Jun Spring 2019 Many slides adapted from Hyoukjun Kwon‘s Gatech “Designing CNN Accelerators ” and 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; . Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . Lecture 14a. Learning layers of features by stacking RBMs. Training a deep . network by stacking RBMs. First train a layer of features that receive input directly from the pixels.. Then treat the activations of the trained features as if they were pixels and learn features of features in a second hidden layer. Dr David Wong. (with thanks to Dr . Gari. Clifford, G.I.T). Overview. What are Artificial Neural Networks (ANNs)?. How do you construct them?. Choosing architecture. Pruning. How do you train them?.