PPT-Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

M Wang T Xiao J Li J Zhang C Hong amp Z Zhang 2014 Presentation by Cameron Hamilton Overview Problem disparity between deep learning tools oriented towards productivitygenerality eg MATLAB and taskspecific tools designed for speed and scale eg CUDA. Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. CS 678 – Deep Learning. 1. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). 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. Deep Learning. Zhiting. Hu. 2014-4-1. Outline. Motivation: why go deep?. DL since 2006. Some DL Models. Discussion. 2. Outline. Motivation: why go deep?. DL since 2006. Some DL Models. Discussion. 3. SINGA Team. September 4, 2015 @VLDB BOSS. Apache SINGA. Outline. Part one. Background. SINGA. Overview. Programming Model. System Architecture. Experimental Study. Research Challenges . Conclusion. Part two. Carey . Nachenberg. Deep Learning for Dummies (Like me) – Carey . Nachenberg. (Like me). The Goal of this Talk?. Deep Learning for Dummies (Like me) – Carey . Nachenberg. 2. To provide you with . Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . 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. CS 501:CS Seminar. Min Xian. Assistant Professor. Department of Computer Science. University of Idaho. Image from NVIDIA. Researchers:. Geoff Hinton. Yann . LeCun. Andrew Ng. Yoshua. . Bengio. …. 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, . Ryota Tomioka (. ryoto@microsoft.com. ). MSR Summer School. 2 July 2018. Azure . iPython. Notebook. https://notebooks.azure.com/ryotat/libraries/DLTutorial. Agenda. This lecture covers. Introduction to machine learning. 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.