Approximate Computing for Deep Learning in TensorFlow Chiang Chi-An T H E U N I V E R S I T Y O F E D I N B U R G H Master of Science School of Informatics University of Edinburgh 2017 Abstract Nowadays, many machine learning techniques are applied on the smart phone […]
C/C++ compilers C/C++ compilers Contents 1 Acorn C/C++ 1 1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
PowerPoint Presentation Dr Massoud Zolgharni mzolgharni@lincoln.ac.uk Room SLB1004, SLB Dr Grzegorz Cielniak gcielniak@lincoln.ac.uk Room INB2221, INB Week W/C Lecture Workshop 1 23/01 Introduction – 2 30/01 Architectures Tutorial-1 3 06/02 Patterns 1 4 13/02 Patterns 2 Tutorial-2 5 20/02 Patterns 3 6 27/02 Patterns 4 Tutorial-3 7 06/03 Communication & Synchronisation 8 13/03 Algorithms 1
09_Rasterisation COMP3421 Rasterisation, Particle Systems The graphics pipeline Projection transformation Illumination Clipping Perspective division ViewportRasterisation Texturing Frame buffer Display Hidden surface removal Model-View Transform Model Transform View Transform Model User Rasterisation Rasterisation is the process of converting lines and polygons represented by their vertices into fragments. Fragments are like pixels but include color,
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Approximate Computing for Deep Learning in TensorFlow Chiang Chi-An T H E U N I V E R S I T Y O F E D I N B U R G H Master of Science School of Informatics University of Edinburgh 2017 Abstract Deep learning techniques have achieved remarkable breakthroughs in many areas such
deepdream DeepDreaming with TensorFlow¶ Loading and displaying the model graph Naive feature visualization Multiscale image generation Laplacian Pyramid Gradient Normalization Playing with feature visualzations DeepDream This notebook demonstrates a number of Convolutional Neural Network image generation techniques implemented with TensorFlow for fun and science: visualize individual feature channels and their combinations to explore the space
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Tensorflow Tensorflow is the second generation machine learning system published by google. It is a successor for google’s previous DistBelief system. Its compution is based on data flow graph with takes math operation as node and multidimensional data arrays (tensors) flows through edges. It is open-sourced and can be used in either single machine or
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (Preliminary White Paper, November 9, 2015) Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg,
MLP Courseworks 3 & 4 Due: 2017-02-16 (cw3); 2017-03-16 (cw4) Machine Learning Practical: Courseworks 3 & 4 Release date Friday 27 January 2017 Due dates 1. Baseline experiments (Coursework 3) – 16:00 Thursday 16th February 2017 2. Advanced experiments (Coursework 4) – 16:00 Thursday 16th March 2017 1 Introduction Courseworks 3 & 4 in MLP
C/C++ compilers C/C++ compilers Contents 1 Acorn C/C++ 1 1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .