PPT-Batch Normalization: Accelerating Deep Network Training by

CS838 Motivation Old school related concept Feature scaling T he range of values of raw training data often varies widely Example Has kids feature in 01 Value of car 500100sk. with MatConvNet. www.cvc.uab.es/~gros/index.php/hands-on-deep-learning-with-matconvnet/. 15. th. January of . 2015. . Training. ‘antelope’. ‘ballet’. ‘boat’. Empirical Risk. Training. Stochastic gradient descent:. ISHAY BE’ERY. ELAD KNOLL. OUTLINES. . Motivation. Model . c. ompression: mimicking large networks:. FITNETS : HINTS FOR THIN DEEP NETS . (A. Romero, 2014). DO DEEP NETS REALLY NEED TO BE DEEP . (Rich Caruana & Lei Jimmy Ba 2014). 李宏. 毅. Hung-yi Lee. Deep learning . attracts . lots of . attention.. Google Trends. Deep learning obtains many exciting results.. 2007. 2009. 2011. 2013. 2015. The talks in this afternoon. This talk will focus on the technical part.. Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. Benchmark and Competition. Cody Coleman. ,. . Deepak . Narayanan, Daniel Kang, Tian . Zhao, Jian Zhang,. Luigi . Nardi. , Peter . Bailis. , . Kunle. . Olukotun. , Chris . Ré. , . Matei. . Zaharia. The Gory Details. (Or, how to be a helicopter parent to a neural network). (Or, why AI is not about to be solved any time soon). Outline. Optimization. Mini-batch SGD. Learning rate decay. Adaptive methods. Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. 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 . Part 1. About me. Or Nachmias. No previous experience in neural networks. Responsible to show the 2. nd. most important lecture in the seminar.. References. Stanford CS231: Convolution Neural Networks for Visual Recognition . Prajit Ramachandran. Outline. Optimization. Regularization. Initialization. Optimization. Optimization Outline. Gradient Descent. Momentum. RMSProp. Adam. Distributed SGD. Gradient Noise. Optimization Outline. Eye-height and Eye-width Estimation Method. Daehwan Lho. Advisor: Prof. . Joungho. Kim. TeraByte Interconnection and Package Laboratory. Department of Electrical Engineering . KAIST. Concept of the Proposed Fast and Accurate Deep . Output. Training Strategy: Batch Normalization. Activation Function: SELU. Network Structure: Highway Network. Batch Normalization. Feature Scaling. ……. ……. ……. ……. ……. ……. ……. Asmitha Rathis. Why Bioinformatics?. Protein structure . Genetic Variants . Anomaly classification . Protein classification. Segmentation/Splicing . Why is Deep Learning beneficial?. scalable with large datasets and are effective in identifying complex patterns from feature-rich datasets . Outline. What is Deep Learning. Tensors: Data Structures for Deep Learning. Multilayer Perceptron. Activation Functions for Deep Learning. Model Training in Deep Learning. Regularization for Deep Learning.