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. forwarding brPage 8br brPage 9br brPage 10br brPage 11br brPage 12br 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. 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. 2015/10/02. 陳柏任. Outline. Neural Networks. Convolutional Neural Networks. Some famous CNN structure. Applications. Toolkit. Conclusion. Reference. 2. Outline. Neural Networks. Convolutional Neural Networks. 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!. . 1. Pushpendre. . Rastogi. Ryan . Cotterell. Jason Eisner. string-to-string transduction. Time flies like an arrow. N. V. P. D. N. Tagging! . Supertagging. !. Morphology!. break. broken. Pronunciation!. Neural networks. Topics. Perceptrons. structure. training. expressiveness. Multilayer networks. possible structures. activation functions. training with gradient descent and . backpropagation. expressiveness. 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 Developing efficient deep neural networks. Forrest Iandola. 1. , Albert Shaw. 2. , Ravi Krishna. 3. , Kurt Keutzer. 4. 1. UC Berkeley → DeepScale → Tesla → Independent Researcher. 2. Georgia Tech → DeepScale → Tesla. 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. Eli Gutin. MIT 15.S60. (adapted from 2016 course by Iain Dunning). Goals today. Go over basics of neural nets. Introduce . TensorFlow. Introduce . Deep Learning. Look at key applications. Practice coding in Python. 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?.