PPT-Tips for Training Deep Network

Output Training Strategy Batch Normalization Activation Function SELU Network Structure Highway Network Batch Normalization Feature Scaling . Naiyan. Wang. Outline. Non-NN Approaches. Deep Convex Net. Extreme Learning Machine. PCAnet. Deep Fisher Net (Already . presented before). Discussion. Deep convex net. Each module is a two- layer convex network.. 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. Dr. Les Deutsch, Dr. Steve Townes. Jet Propulsion Laboratory, California Institute of Technology. Phil . Liebrecht. , Pete Vrotsos, Dr. Don Cornwell. National Aeronautics and Space Administration. © . 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.. Rajdeep. . Dasgupta. CIDER Community Workshop, CA. May 08, 2016. Volcanic degassing. hazards. long-term climate. Bio-essential elements. Origin of life. Mantle melting. Chemical differentiation. Properties of asthenosphere. Reading and Research in Deep Learning. James K Baker. 11-364 Deep Learning R&R. Hands-on Tutorial Books with Sample Code. Leading Edge Research Papers. Background Tasks. Learn the core concepts of Deep Learning. 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, . Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. Indian stock market trading tips for daily gains
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Stock market tips, Share market tips, Share tips, Mcx tips 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/ . 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.