PPT-Tips for Training Deep Network

Output Training Strategy Batch Normalization Activation Function SELU Network Structure Highway Network Batch Normalization Feature Scaling . 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. Glyn Williams. . . Zero Sum Game. Time spent on training = . time taken away from PhD. …or…. Zero Sum Game. Time spent on training = . time taken away from PhD. Symbiosis?. …or…. . . Your PhD. Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . 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. 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. Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . 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 . DistributedattackdetectionschemeusingdeeplearningapproachforInternetofThingsAbebeAbeshuDiro,NaveenChilamkurtiPII:S0167-739X(17)30848-8DOI:http://dx.doi.org/10.1016/j.future.2017.08.043Reference:FUTURE 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 . 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/ . 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. 1. Deep Learning. Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). Multiple layers work to build an improved feature space.