Defense Data Generation in Distributed Deep Learning System Se-Yoon Oh / ADD-IDAR
|
|
|
- Matthew Carpenter
- 5 years ago
- Views:
Transcription
1 Defense Data Generation in Distributed Deep Learning System Se-Yoon Oh / syoh@add.re.kr Page 1/36
2 Overview 1. Introduction 2. Data Generation Synthesis 3. Distributed Deep Learning 4. Conclusions Page 2/36
3 ML Process (Supervised Learning, DNN) 1 Labeled Training Images Train Model on GPU Accelerated Cluster input conv 2 Neural Network Framework 4 Trained Network conv pool fullycon fullycon softmax 3 NN Model Definition model.add(convolution2d(128, 3, 3) CAT Page 3/36
4 ML Process (Supervised Learning, DNN) 1 2 Labeled Training Images Neural Network Framework Benchmark DB? Transfer learning? One-shot learning? 3 NN Model Definition model.add(convolution2d(128, 3, 3) 4 Train Model on GPU Accelerated Cluster Page 4/36
5 NN Hyper-Parameter Page 5/36
6 ImageNet Classification with Deep Convolutional Neural Networks (Alex Krizhevsky,2012) - 650,000 neurons, 60,000,000 parameters M images(224*224*3), 1,000 classes - 7 Hidden layers, ReLU, DropOut, 2 GPU(GTX580)/week - Overlapped max pooling, Augmentation 1 st Layer ConV L : 90~95% 연산량 (5% Para) - 290,400 개 (N) Full L : 5~10% 연산량 (95% Para) - ((11*11*3)+1) * 96 = 34,944 개 (P) - 290,400 * 364 = 105,750,600 개 (C) Page 6/36
7 Tech. Trends Page 7/36
8 Supervised learning(sl) Standard in the current state of ML Performed well on many task Ground-truth annotation/label is required in training Unsupervised learning(ul) How most learning is done in the real world Impractical to accurately annotate data UL seems to be the key to real artificial intelligence Transfer learning & fine-tuning CNN -> Training with millions of unlabeled images(ul) -> Deep understanding in images Quickly learn the new task with much smaller labelled images (?) Page 8/36
9 Two Things Considered Labeled data Few labeled data in defense Classification/object detection/ segmentation/tracking/autonomy/ Computing power ML/DL based on HPC & GPU Big data hub & data analytics Page 9/36
10 Overview 1. Introduction 2. Data Generation Synthesis 3. Distributed Deep Learning 4. Conclusions Page 10/36
11 Problem Statement ML demands access to very large sets of training data Significant problem may arise in real-world defense ML applications To overcome problems of small-data, novel synthetic data generation frameworks for massive ML training system are required Page 11/36
12 Page 12/36
13 Page 13/36
14 Page 14/36
15 Objectives & Motivation Develop a massive ML training dataset generation/synthesis system 1) Automatic Data Augmentation framework 2) 3D CAD modeling based synthetic/virtual image datasets generation framework 3) Game Development Engine based virtual scenes/military env. creation framework 4) Generative Models(GANs) framework for photorealistic images tuning Page 15/36
16 Generative Adversarial Networks(GANs) Data : a dataset as samples from a true data distribution Generator : Random noise as input, and transforms it, outputting a sample of data Discriminator : Classifies sample input data as real(from true data distribution) or fake(from the generator), x = g(z) Play a song on guitar - Song (Data) - Hands/brain (G) - Ears/brain (D) Page 16/36
17 GANs Latent Space Variation AYLIEN 2016 PPGN 2016 / Evolving AI Lab Yann Le Cun China Page 17/36
18 Difficulties Mode collapsing Oscillating during training No learning CycleGAN Ian Goodfellow GTC 2017 (Zhu et al., 2017) Page 18/36
19 GANs-based Image Generation Dataset: 1,300 images of Army-tank (ILSVRC2012) Model: DCGAN Real army-tank training images(imagenet DB) Generated synthetic armytank images (Epoch=189) Page 19/36
20 GANs-based Image Generation Changes of generated synthetic images along with iterations Epoch=5 Epoch=26 Epoch=99 Epoch=189 Page 20/36
21 Overview 1. Introduction 2. Data Generation Synthesis 3. Distributed Deep Learning 4. Conclusions Page 21/36
22 Grade Examination 50 EA 200 EA 10,000 EA Page 22/36
23 Grade Examination 10,000 EA Employ 50 P Grading/ Management Employee Mgt. : absence/medical/missing/ General Management Management/processing/copying/data missing/summary/reporting/ Distributed Computing Big Data Computing : Apache-Hadoop S/W Page 23/36
24 Sizes of data units ZB/2yr/2 배 /40ZB(2020) 57*Sand 24PB Google/Day/ ZB/2%/2013 1TB 80 Mb/s) 1 disk hr 100 disks - 2 min 1000 disks - 12 sec Page 24/36
25 Big Data / AI Big Data A I Page 25/36
26 RDBMS 1Terabyte/200$, Hadoop 1Terabyte/5$ RDB Big Data DB Library Logistics Page 26/36
27 Big Data Component (Hadoop) Infomatica/Talend/InfoShere/DataStage/Apach e.hadoop/spark/avro/ambari/chukwa/mahout /Tez/Yarn/Zookeeper/Pig/Hive/MapReduce/Hc atalog/impalra/kafka/hbase/singa/stinger/m apr/aster/splunk/cassandra/mongo/riak/vor mic/hana/esper/oracle/tibco/r/sas/spss/ta bleau/spotfire/d3/qliktech/microstrategy/exa data/greenplum/netizza/radian6/sma/cloudb igdata/amazon/softlayer/rackspac/cloudant/ Dynamo/Teradata cloud/ Page 27/36
28 Apache Spark : V : V : V : V : V : V : V : V : V Google TensorFlow ( 15.11) Page 28/36
29 Distributed Computing Data/Model parallelism Full-mesh/Star topology ETRI E/T Trends ( 안신영 et al. 2016) Page 29/36
30 - 1,200,000 Image - 1,000 Classes [Image Big Data : ImageNet DB] Trained w, b Image(x) Label(y) Computer (Training) NN Model Page 30/36
31 DNN Error w.r.t Hidden Layer DNN Layer Error Year Remark % 2011 AlexNet % 2012 VGG % 2014 GoogleNet % 2014 ResNet % % , % 2016 Benchmark DNN - ResNet-200 (2016) - ImageNet (1.2M, 1,000Class) 200 Layers Page 31/36
32 Computing Time w.r.t GPU GPU Petaflops (FP32) Time Remark Case 1 Case Days - 2, hrs AIST- AIRC (J) Days ,500 65~ min 0.7 PF * 90(P) Tsubame 3(J) HPE 540 Node Tesla P100 : 2,160 HPC with 8 GPU 3 Weeks (ResNet 200 Layer) ImageNet 1.2M RGB Images with 1,000 classes 200,000 USD for 8-GPU HPC Page 32/36
33 BD/DL System : Open Source Platform Architecture Component Applications Vision / Speech / Behavior / DL Framework Libraries(SDK) System Software Hardware Caffe, Torch, Theano, DL4J, CNTK, Pylearn2, TensorFlow, KERAS, cudnn, NCCL, IMSL Lib., GPU VSIPL, cublas, cusparse, cufft Apache Hadoop, Spark, Ambari, HBase, Yarn, Drivers, CUDA, Clustered Computer (Linux) X86, GP-GPU Page 33/36
34 Overview 1. Introduction 2. Data Generation Synthesis 3. Distributed Deep Learning 4. Conclusions Page 34/36
35 Two Things Considered Labeled data Few labeled data in defense Classification/object detection/ segmentation/tracking/autonomy/ Synthetic/virtual data generation Computing power ML/DL based on HPC & GPU Big data hub & data analytics Distributed deep learning Page 35/36
36 Artificial Intelligence (Machine Learning,...) Autonomous System Learning Platform (Vision/Speech/Behavior) VR/AR Design & Analysis Analytics AI-Learning Intelligent Distributed Computing Big Data based AI Platform Page 36/36
Deep Learning with Tensorflow AlexNet
Machine Learning and Computer Vision Group Deep Learning with Tensorflow http://cvml.ist.ac.at/courses/dlwt_w17/ AlexNet Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification
HPE Deep Learning Cookbook: Recipes to Run Deep Learning Workloads. Natalia Vassilieva, Sergey Serebryakov
HPE Deep Learning Cookbook: Recipes to Run Deep Learning Workloads Natalia Vassilieva, Sergey Serebryakov Deep learning ecosystem today Software Hardware 2 HPE s portfolio for deep learning Government,
Machine Learning With Python. Bin Chen Nov. 7, 2017 Research Computing Center
Machine Learning With Python Bin Chen Nov. 7, 2017 Research Computing Center Outline Introduction to Machine Learning (ML) Introduction to Neural Network (NN) Introduction to Deep Learning NN Introduction
Characterization and Benchmarking of Deep Learning. Natalia Vassilieva, PhD Sr. Research Manager
Characterization and Benchmarking of Deep Learning Natalia Vassilieva, PhD Sr. Research Manager Deep learning applications Vision Speech Text Other Search & information extraction Security/Video surveillance
Fuzzy Set Theory in Computer Vision: Example 3
Fuzzy Set Theory in Computer Vision: Example 3 Derek T. Anderson and James M. Keller FUZZ-IEEE, July 2017 Overview Purpose of these slides are to make you aware of a few of the different CNN architectures
DEEP NEURAL NETWORKS CHANGING THE AUTONOMOUS VEHICLE LANDSCAPE. Dennis Lui August 2017
DEEP NEURAL NETWORKS CHANGING THE AUTONOMOUS VEHICLE LANDSCAPE Dennis Lui August 2017 THE RISE OF GPU COMPUTING APPLICATIONS 10 7 10 6 GPU-Computing perf 1.5X per year 1000X by 2025 ALGORITHMS 10 5 1.1X
An introduction to Machine Learning silicon
An introduction to Machine Learning silicon November 28 2017 Insight for Technology Investors AI/ML terminology Artificial Intelligence Machine Learning Deep Learning Algorithms: CNNs, RNNs, etc. Additional
POINT CLOUD DEEP LEARNING
POINT CLOUD DEEP LEARNING Innfarn Yoo, 3/29/28 / 57 Introduction AGENDA Previous Work Method Result Conclusion 2 / 57 INTRODUCTION 3 / 57 2D OBJECT CLASSIFICATION Deep Learning for 2D Object Classification
Generative Modeling with Convolutional Neural Networks. Denis Dus Data Scientist at InData Labs
Generative Modeling with Convolutional Neural Networks Denis Dus Data Scientist at InData Labs What we will discuss 1. 2. 3. 4. Discriminative vs Generative modeling Convolutional Neural Networks How to
A NEW COMPUTING ERA JENSEN HUANG, FOUNDER & CEO GTC CHINA 2017
A NEW COMPUTING ERA JENSEN HUANG, FOUNDER & CEO GTC CHINA 2017 TWO FORCES DRIVING THE FUTURE OF COMPUTING 10 7 Transistors (thousands) 10 6 10 5 1.1X per year 10 4 10 3 10 2 1.5X per year Single-threaded
NVIDIA FOR DEEP LEARNING. Bill Veenhuis
NVIDIA FOR DEEP LEARNING Bill Veenhuis bveenhuis@nvidia.com Nvidia is the world s leading ai platform ONE ARCHITECTURE CUDA 2 GPU: Perfect Companion for Accelerating Apps & A.I. CPU GPU 3 Intro to AI AGENDA
Object recognition and computer vision using MATLAB and NVIDIA Deep Learning SDK
Object recognition and computer vision using MATLAB and NVIDIA Deep Learning SDK 17 May 2016, Melbourne 24 May 2016, Sydney Werner Scholz, CTO and Head of R&D, XENON Systems Mike Wang, Solutions Architect,
A performance comparison of Deep Learning frameworks on KNL
A performance comparison of Deep Learning frameworks on KNL R. Zanella, G. Fiameni, M. Rorro Middleware, Data Management - SCAI - CINECA IXPUG Bologna, March 5, 2018 Table of Contents 1. Problem description
GPU FOR DEEP LEARNING. 周国峰 Wuhan University 2017/10/13
GPU FOR DEEP LEARNING chandlerz@nvidia.com 周国峰 Wuhan University 2017/10/13 Why Deep Learning Boost Today? Nvidia SDK for Deep Learning? Agenda CUDA 8.0 cudnn TensorRT (GIE) NCCL DIGITS 2 Why Deep Learning
Deep Neural Network Hyperparameter Optimization with Genetic Algorithms
Deep Neural Network Hyperparameter Optimization with Genetic Algorithms EvoDevo A Genetic Algorithm Framework Aaron Vose, Jacob Balma, Geert Wenes, and Rangan Sukumar Cray Inc. October 2017 Presenter Vose,
DEEP NEURAL NETWORKS AND GPUS. Julie Bernauer
DEEP NEURAL NETWORKS AND GPUS Julie Bernauer GPU Computing GPU Computing Run Computations on GPUs x86 CUDA Framework to Program NVIDIA GPUs A simple sum of two vectors (arrays) in C void vector_add(int
All You Want To Know About CNNs. Yukun Zhu
All You Want To Know About CNNs Yukun Zhu Deep Learning Deep Learning Image from http://imgur.com/ Deep Learning Image from http://imgur.com/ Deep Learning Image from http://imgur.com/ Deep Learning Image
NVIDIA DLI HANDS-ON TRAINING COURSE CATALOG
NVIDIA DLI HANDS-ON TRAINING COURSE CATALOG Valid Through July 31, 2018 INTRODUCTION The NVIDIA Deep Learning Institute (DLI) trains developers, data scientists, and researchers on how to use artificial
Deep Learning for Computer Vision II
IIIT Hyderabad Deep Learning for Computer Vision II C. V. Jawahar Paradigm Shift Feature Extraction (SIFT, HoG, ) Part Models / Encoding Classifier Sparrow Feature Learning Classifier Sparrow L 1 L 2 L
Introduction to Deep Learning in Signal Processing & Communications with MATLAB
Introduction to Deep Learning in Signal Processing & Communications with MATLAB Dr. Amod Anandkumar Pallavi Kar Application Engineering Group, Mathworks India 2019 The MathWorks, Inc. 1 Different Types
DEEP LEARNING WITH GPUS Maxim Milakov, Senior HPC DevTech Engineer, NVIDIA
DEEP LEARNING WITH GPUS Maxim Milakov, Senior HPC DevTech Engineer, NVIDIA TOPICS COVERED Convolutional Networks Deep Learning Use Cases GPUs cudnn 2 MACHINE LEARNING! Training! Train the model from supervised
IBM Deep Learning Solutions
IBM Deep Learning Solutions Reference Architecture for Deep Learning on POWER8, P100, and NVLink October, 2016 How do you teach a computer to Perceive? 2 Deep Learning: teaching Siri to recognize a bicycle
Alternatives to Direct Supervision
CreativeAI: Deep Learning for Graphics Alternatives to Direct Supervision Niloy Mitra Iasonas Kokkinos Paul Guerrero Nils Thuerey Tobias Ritschel UCL UCL UCL TUM UCL Timetable Theory and Basics State of
Tutorial on Keras CAP ADVANCED COMPUTER VISION SPRING 2018 KISHAN S ATHREY
Tutorial on Keras CAP 6412 - ADVANCED COMPUTER VISION SPRING 2018 KISHAN S ATHREY Deep learning packages TensorFlow Google PyTorch Facebook AI research Keras Francois Chollet (now at Google) Chainer Company
CS 2750: Machine Learning. Neural Networks. Prof. Adriana Kovashka University of Pittsburgh April 13, 2016
CS 2750: Machine Learning Neural Networks Prof. Adriana Kovashka University of Pittsburgh April 13, 2016 Plan for today Neural network definition and examples Training neural networks (backprop) Convolutional
Beyond Training The next steps of Machine Learning. Chris /in/chrisparsonsdev
Beyond Training The next steps of Machine Learning Chris Parsons chrisparsons@uk.ibm.com @chrisparsonsdev /in/chrisparsonsdev What is this talk? Part 1 What is Machine Learning? AI Infrastructure PowerAI
Cisco UCS C480 ML M5 Rack Server Performance Characterization
White Paper Cisco UCS C480 ML M5 Rack Server Performance Characterization The Cisco UCS C480 ML M5 Rack Server platform is designed for artificial intelligence and machine-learning workloads. 2018 Cisco
Deep Learning for Computer Vision with MATLAB By Jon Cherrie
Deep Learning for Computer Vision with MATLAB By Jon Cherrie 2015 The MathWorks, Inc. 1 Deep learning is getting a lot of attention "Dahl and his colleagues won $22,000 with a deeplearning system. 'We
Unsupervised Learning
Deep Learning for Graphics Unsupervised Learning Niloy Mitra Iasonas Kokkinos Paul Guerrero Vladimir Kim Kostas Rematas Tobias Ritschel UCL UCL/Facebook UCL Adobe Research U Washington UCL Timetable Niloy
Why data science is the new frontier in software development
Why data science is the new frontier in software development And why every developer should care Jeff Prosise jeffpro@wintellect.com @jprosise Assertion #1 Being a programmer is like being the god of your
Lab meeting (Paper review session) Stacked Generative Adversarial Networks
Lab meeting (Paper review session) Stacked Generative Adversarial Networks 2017. 02. 01. Saehoon Kim (Ph. D. candidate) Machine Learning Group Papers to be covered Stacked Generative Adversarial Networks
TOWARDS ACCELERATED DEEP LEARNING IN HPC AND HYPERSCALE ARCHITECTURES Environnement logiciel pour l apprentissage profond dans un contexte HPC
TOWARDS ACCELERATED DEEP LEARNING IN HPC AND HYPERSCALE ARCHITECTURES Environnement logiciel pour l apprentissage profond dans un contexte HPC TERATECH Juin 2017 Gunter Roth, François Courteille DRAMATIC
Pouya Kousha Fall 2018 CSE 5194 Prof. DK Panda
Pouya Kousha Fall 2018 CSE 5194 Prof. DK Panda 1 Motivation And Intro Programming Model Spark Data Transformation Model Construction Model Training Model Inference Execution Model Data Parallel Training
Deep Learning Frameworks with Spark and GPUs
Deep Learning Frameworks with Spark and GPUs Abstract Spark is a powerful, scalable, real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel,
Convolutional Neural Networks. Computer Vision Jia-Bin Huang, Virginia Tech
Convolutional Neural Networks Computer Vision Jia-Bin Huang, Virginia Tech Today s class Overview Convolutional Neural Network (CNN) Training CNN Understanding and Visualizing CNN Image Categorization:
COMP9444 Neural Networks and Deep Learning 7. Image Processing. COMP9444 c Alan Blair, 2017
COMP9444 Neural Networks and Deep Learning 7. Image Processing COMP9444 17s2 Image Processing 1 Outline Image Datasets and Tasks Convolution in Detail AlexNet Weight Initialization Batch Normalization
Know your data - many types of networks
Architectures Know your data - many types of networks Fixed length representation Variable length representation Online video sequences, or samples of different sizes Images Specific architectures for
Deep Learning Basic Lecture - Complex Systems & Artificial Intelligence 2017/18 (VO) Asan Agibetov, PhD.
Deep Learning 861.061 Basic Lecture - Complex Systems & Artificial Intelligence 2017/18 (VO) Asan Agibetov, PhD asan.agibetov@meduniwien.ac.at Medical University of Vienna Center for Medical Statistics,
Object Detection Lecture Introduction to deep learning (CNN) Idar Dyrdal
Object Detection Lecture 10.3 - Introduction to deep learning (CNN) Idar Dyrdal Deep Learning Labels Computational models composed of multiple processing layers (non-linear transformations) Used to learn
NVIDIA GPU CLOUD DEEP LEARNING FRAMEWORKS
TECHNICAL OVERVIEW NVIDIA GPU CLOUD DEEP LEARNING FRAMEWORKS A Guide to the Optimized Framework Containers on NVIDIA GPU Cloud Introduction Artificial intelligence is helping to solve some of the most
NVIDIA DEEP LEARNING INSTITUTE
NVIDIA DEEP LEARNING INSTITUTE TRAINING CATALOG Valid Through July 31, 2018 INTRODUCTION The NVIDIA Deep Learning Institute (DLI) trains developers, data scientists, and researchers on how to use artificial
Intro to Deep Learning. Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn
Intro to Deep Learning Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn Why this class? Deep Features Have been able to harness the big data in the most efficient and effective
In partnership with. VelocityAI REFERENCE ARCHITECTURE WHITE PAPER
In partnership with VelocityAI REFERENCE JULY // 2018 Contents Introduction 01 Challenges with Existing AI/ML/DL Solutions 01 Accelerate AI/ML/DL Workloads with Vexata VelocityAI 02 VelocityAI Reference
MoonRiver: Deep Neural Network in C++
MoonRiver: Deep Neural Network in C++ Chung-Yi Weng Computer Science & Engineering University of Washington chungyi@cs.washington.edu Abstract Artificial intelligence resurges with its dramatic improvement
Unified Deep Learning with CPU, GPU, and FPGA Technologies
Unified Deep Learning with CPU, GPU, and FPGA Technologies Allen Rush 1, Ashish Sirasao 2, Mike Ignatowski 1 1: Advanced Micro Devices, Inc., 2: Xilinx, Inc. Abstract Deep learning and complex machine
ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey Hinton University of Toronto Canada Paper with same name to appear in NIPS 2012 Main idea Architecture
Deep Learning in Visual Recognition. Thanks Da Zhang for the slides
Deep Learning in Visual Recognition Thanks Da Zhang for the slides Deep Learning is Everywhere 2 Roadmap Introduction Convolutional Neural Network Application Image Classification Object Detection Object
Deep Learning. Volker Tresp Summer 2017
Deep Learning Volker Tresp Summer 2017 1 Neural Network Winter and Revival While Machine Learning was flourishing, there was a Neural Network winter (late 1990 s until late 2000 s) Around 2010 there
INTRODUCTION TO DEEP LEARNING
INTRODUCTION TO DEEP LEARNING CONTENTS Introduction to deep learning Contents 1. Examples 2. Machine learning 3. Neural networks 4. Deep learning 5. Convolutional neural networks 6. Conclusion 7. Additional
Real Time Monitoring of CCTV Camera Images Using Object Detectors and Scene Classification for Retail and Surveillance Applications
Real Time Monitoring of CCTV Camera Images Using Object Detectors and Scene Classification for Retail and Surveillance Applications Anand Joshi CS229-Machine Learning, Computer Science, Stanford University,
Structured Prediction using Convolutional Neural Networks
Overview Structured Prediction using Convolutional Neural Networks Bohyung Han bhhan@postech.ac.kr Computer Vision Lab. Convolutional Neural Networks (CNNs) Structured predictions for low level computer
Review: The best frameworks for machine learning and deep learning
Review: The best frameworks for machine learning and deep learning infoworld.com/article/3163525/analytics/review-the-best-frameworks-for-machine-learning-and-deep-learning.html By Martin Heller Over the
DEEP LEARNING ALISON B LOWNDES. Deep Learning Solutions Architect & Community Manager EMEA
DEEP LEARNING ALISON B LOWNDES Deep Learning Solutions Architect & Community Manager EMEA 1 THE GPU-ACCELERATED WORLD HPC DEEP LEARNING PC VIRTUALIZATION CLOUD GAMING RENDERING 2 3 Why is Deep Learning
GPU-Accelerated Deep Learning
GPU-Accelerated Deep Learning July 6 th, 2016. Greg Heinrich. Credits: Alison B. Lowndes, Julie Bernauer, Leo K. Tam. PRACTICAL DEEP LEARNING EXAMPLES Image Classification, Object Detection, Localization,
Spatial Localization and Detection. Lecture 8-1
Lecture 8: Spatial Localization and Detection Lecture 8-1 Administrative - Project Proposals were due on Saturday Homework 2 due Friday 2/5 Homework 1 grades out this week Midterm will be in-class on Wednesday
TESLA V100 PERFORMANCE GUIDE. Life Sciences Applications
TESLA V100 PERFORMANCE GUIDE Life Sciences Applications NOVEMBER 2017 TESLA V100 PERFORMANCE GUIDE Modern high performance computing (HPC) data centers are key to solving some of the world s most important
Machine Learning. MGS Lecture 3: Deep Learning
Dr Michel F. Valstar http://cs.nott.ac.uk/~mfv/ Machine Learning MGS Lecture 3: Deep Learning Dr Michel F. Valstar http://cs.nott.ac.uk/~mfv/ WHAT IS DEEP LEARNING? Shallow network: Only one hidden layer
Perceptron: This is convolution!
Perceptron: This is convolution! v v v Shared weights v Filter = local perceptron. Also called kernel. By pooling responses at different locations, we gain robustness to the exact spatial location of image
Machine Learning. Deep Learning. Eric Xing (and Pengtao Xie) , Fall Lecture 8, October 6, Eric CMU,
Machine Learning 10-701, Fall 2015 Deep Learning Eric Xing (and Pengtao Xie) Lecture 8, October 6, 2015 Eric Xing @ CMU, 2015 1 A perennial challenge in computer vision: feature engineering SIFT Spin image
DEEP LEARNING AND DIGITS DEEP LEARNING GPU TRAINING SYSTEM
DEEP LEARNING AND DIGITS DEEP LEARNING GPU TRAINING SYSTEM AGENDA 1 Introduction to Deep Learning 2 What is DIGITS 3 How to use DIGITS Practical DEEP LEARNING Examples Image Classification, Object Detection,
GPU Coder: Automatic CUDA and TensorRT code generation from MATLAB
GPU Coder: Automatic CUDA and TensorRT code generation from MATLAB Ram Kokku 2018 The MathWorks, Inc. 1 GPUs and CUDA programming faster Performance CUDA OpenCL C/C++ GPU Coder MATLAB Python Ease of programming
A NEW COMPUTING ERA. DAVID B. KIRK, FELLOW NVIDIA AI Conference Singapore 2017
A NEW COMPUTING ERA DAVID B. KIRK, FELLOW NVIDIA AI Conference Singapore 2017 TWO FORCES DRIVING THE FUTURE OF COMPUTING 10 7 Transistors (thousands) 10 5 1.1X per year 10 3 1.5X per year Single-threaded
Deep Learning Workshop. Nov. 20, 2015 Andrew Fishberg, Rowan Zellers
Deep Learning Workshop Nov. 20, 2015 Andrew Fishberg, Rowan Zellers Why deep learning? The ImageNet Challenge Goal: image classification with 1000 categories Top 5 error rate of 15%. Krizhevsky, Alex,
Deep Learning. Visualizing and Understanding Convolutional Networks. Christopher Funk. Pennsylvania State University.
Visualizing and Understanding Convolutional Networks Christopher Pennsylvania State University February 23, 2015 Some Slide Information taken from Pierre Sermanet (Google) presentation on and Computer
Deep learning in MATLAB From Concept to CUDA Code
Deep learning in MATLAB From Concept to CUDA Code Roy Fahn Applications Engineer Systematics royf@systematics.co.il 03-7660111 Ram Kokku Principal Engineer MathWorks ram.kokku@mathworks.com 2017 The MathWorks,
High Performance Computing
High Performance Computing 9th Lecture 2016/10/28 YUKI ITO 1 Selected Paper: vdnn: Virtualized Deep Neural Networks for Scalable, MemoryEfficient Neural Network Design Minsoo Rhu, Natalia Gimelshein, Jason
Sony's deep learning software "Neural Network Libraries/Console and its use cases in Sony
GTC 2018 Sony's deep learning software "Neural Network Libraries/Console and its use cases in Sony Yoshiyuki Kobayashi Senior Machine Learning Researcher Sony Network Communications Inc. / Sony Corporation
Making Sense of Artificial Intelligence: A Practical Guide
Making Sense of Artificial Intelligence: A Practical Guide JEDEC Mobile & IOT Forum Copyright 2018 Young Paik, Samsung Senior Director Product Planning Disclaimer This presentation and/or accompanying
Mocha.jl. Deep Learning in Julia. Chiyuan Zhang CSAIL, MIT
Mocha.jl Deep Learning in Julia Chiyuan Zhang (@pluskid) CSAIL, MIT Deep Learning Learning with multi-layer (3~30) neural networks, on a huge training set. State-of-the-art on many AI tasks Computer Vision:
Deep Learning for Embedded Security Evaluation
Deep Learning for Embedded Security Evaluation Emmanuel Prouff 1 1 Laboratoire de Sécurité des Composants, ANSSI, France April 2018, CISCO April 2018, CISCO E. Prouff 1/22 Contents 1. Context and Motivation
Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 9-1 Administrative A2 due Wed May 2 Midterm: In-class Tue May 8. Covers material through Lecture 10 (Thu May 3). Sample midterm released on piazza. Midterm review session: Fri May 4 discussion
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks Naveen Suda, Vikas Chandra *, Ganesh Dasika *, Abinash Mohanty, Yufei Ma, Sarma Vrudhula, Jae-sun Seo, Yu
Autoencoders. Stephen Scott. Introduction. Basic Idea. Stacked AE. Denoising AE. Sparse AE. Contractive AE. Variational AE GAN.
Stacked Denoising Sparse Variational (Adapted from Paul Quint and Ian Goodfellow) Stacked Denoising Sparse Variational Autoencoding is training a network to replicate its input to its output Applications:
POWERING THE AI REVOLUTION JENSEN HUANG, FOUNDER & CEO GTC 2017
POWERING THE AI REVOLUTION JENSEN HUANG, FOUNDER & CEO GTC 2017 LIFE AFTER MOORE S LAW 10 7 40 Years of Microprocessor Trend Data 10 6 10 5 Transistors (thousands) 1.1X per year 10 4 10 3 1.5X per year
Deep Learning mit PowerAI - Ein Überblick
Stephen Lutz Deep Learning mit PowerAI - Open Group Master Certified IT Specialist Technical Sales IBM Cognitive Infrastructure IBM Germany Ein Überblick Stephen.Lutz@de.ibm.com What s that? and what s
Convolutional Neural Networks
NPFL114, Lecture 4 Convolutional Neural Networks Milan Straka March 25, 2019 Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics unless otherwise
Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning
Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning Ammar Ahmad Awan, Khaled Hamidouche, Akshay Venkatesh, and Dhabaleswar K. Panda Network-Based Computing Laboratory Department
EFFICIENT INFERENCE WITH TENSORRT. Han Vanholder
EFFICIENT INFERENCE WITH TENSORRT Han Vanholder AI INFERENCING IS EXPLODING 2 Trillion Messages Per Day On LinkedIn 500M Daily active users of iflytek 140 Billion Words Per Day Translated by Google 60
Object Detection Based on Deep Learning
Object Detection Based on Deep Learning Yurii Pashchenko AI Ukraine 2016, Kharkiv, 2016 Image classification (mostly what you ve seen) http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
Deep Learning and Its Applications
Convolutional Neural Network and Its Application in Image Recognition Oct 28, 2016 Outline 1 A Motivating Example 2 The Convolutional Neural Network (CNN) Model 3 Training the CNN Model 4 Issues and Recent
High-Performance Training for Deep Learning and Computer Vision HPC
High-Performance Training for Deep Learning and Computer Vision HPC Panel at CVPR-ECV 18 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
Study of Residual Networks for Image Recognition
Study of Residual Networks for Image Recognition Mohammad Sadegh Ebrahimi Stanford University sadegh@stanford.edu Hossein Karkeh Abadi Stanford University hosseink@stanford.edu Abstract Deep neural networks
Brainchip OCTOBER
Brainchip OCTOBER 2017 1 Agenda Neuromorphic computing background Akida Neuromorphic System-on-Chip (NSoC) Brainchip OCTOBER 2017 2 Neuromorphic Computing Background Brainchip OCTOBER 2017 3 A Brief History
Fine-tuning Pre-trained Large Scaled ImageNet model on smaller dataset for Detection task
Fine-tuning Pre-trained Large Scaled ImageNet model on smaller dataset for Detection task Kyunghee Kim Stanford University 353 Serra Mall Stanford, CA 94305 kyunghee.kim@stanford.edu Abstract We use a
Profiling DNN Workloads on a Volta-based DGX-1 System
Profiling DNN Workloads on a Volta-based DGX-1 System Saiful A. Mojumder 1, Marcia S Louis 1, Yifan Sun 2, Amir Kavyan Ziabari 3, José L. Abellán 4, John Kim 5, David Kaeli 2, Ajay Joshi 1 1 ECE Department,
Characterizing and Benchmarking Deep Learning Systems on Modern Data Center Architectures
Characterizing and Benchmarking Deep Learning Systems on Modern Data Center Architectures Talk at Bench 2018 by Xiaoyi Lu The Ohio State University E-mail: luxi@cse.ohio-state.edu http://www.cse.ohio-state.edu/~luxi
Como funciona o Deep Learning
Como funciona o Deep Learning Moacir Ponti (com ajuda de Gabriel Paranhos da Costa) ICMC, Universidade de São Paulo Contact: www.icmc.usp.br/~moacir moacir@icmc.usp.br Uberlandia-MG/Brazil October, 2017
CNN optimization. Rassadin A
CNN optimization Rassadin A. 01.2017-02.2017 What to optimize? Training stage time consumption (CPU / GPU) Inference stage time consumption (CPU / GPU) Training stage memory consumption Inference stage
COMP 551 Applied Machine Learning Lecture 16: Deep Learning
COMP 551 Applied Machine Learning Lecture 16: Deep Learning Instructor: Ryan Lowe (ryan.lowe@cs.mcgill.ca) Slides mostly by: Class web page: www.cs.mcgill.ca/~hvanho2/comp551 Unless otherwise noted, all
Training Deep Neural Networks (in parallel)
Lecture 9: Training Deep Neural Networks (in parallel) Visual Computing Systems How would you describe this professor? Easy? Mean? Boring? Nerdy? Professor classification task Classifies professors as
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
1 Deep Learning for IoT Big Data and Streaming Analytics: A Survey Mehdi Mohammadi, Graduate Student Member, IEEE, Ala Al-Fuqaha, Senior Member, IEEE, Sameh Sorour, Senior Member, IEEE, Mohsen Guizani,
PRACTICAL SCALING TECHNIQUES. Ujval Kapasi Dec 9, 2017
PRACTICAL SCALING TECHNIQUES Ujval Kapasi Dec 9, 2017 DNN TRAINING ON MULTIPLE GPUS Making DL training times shorter 2 DNN TRAINING ON MULTIPLE GPUS Making DL training times shorter local local local Allreduce
CENG 783. Special topics in. Deep Learning. AlchemyAPI. Week 11. Sinan Kalkan
CENG 783 Special topics in Deep Learning AlchemyAPI Week 11 Sinan Kalkan TRAINING A CNN Fig: http://www.robots.ox.ac.uk/~vgg/practicals/cnn/ Feed-forward pass Note that this is written in terms of the
Deep learning in action with DL4J
Deep learning in action with DL4J Sigrid Keydana Trivadis München Keywords Deep Learning, Machine Learning, Artificial Intelligence, DL4J, Deeplearning4j, Java, Anomaly Detection Introduction In this second
Joint Object Detection and Viewpoint Estimation using CNN features
Joint Object Detection and Viewpoint Estimation using CNN features Carlos Guindel, David Martín and José M. Armingol cguindel@ing.uc3m.es Intelligent Systems Laboratory Universidad Carlos III de Madrid
Convolutional Neural Networks
Lecturer: Barnabas Poczos Introduction to Machine Learning (Lecture Notes) Convolutional Neural Networks Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications.
Convolutional Neural Network based Medical Imaging Segmentation: Recent Progress and Challenges. Jiaxing Tan
Convolutional Neural Network based Medical Imaging Segmentation: Recent Progress and Challenges Jiaxing Tan Road Map Introduction CNN based Models Encoder-Decoder based Models GAN Based Models Some Challenges
Code Mania Artificial Intelligence: a. Module - 1: Introduction to Artificial intelligence and Python:
Code Mania 2019 Artificial Intelligence: a. Module - 1: Introduction to Artificial intelligence and Python: 1. Introduction to Artificial Intelligence 2. Introduction to python programming and Environment
Demystifying Deep Learning
Demystifying Deep Learning Mandar Gujrathi Mandar.Gujrathi@mathworks.com.au 2015 The MathWorks, Inc. 1 2 Deep Learning Applications Voice assistants (speech to text) Teaching character to beat video game
Exploiting the OpenPOWER Platform for Big Data Analytics and Cognitive. Rajesh Bordawekar and Ruchir Puri IBM T. J. Watson Research Center
Exploiting the OpenPOWER Platform for Big Data Analytics and Cognitive Rajesh Bordawekar and Ruchir Puri IBM T. J. Watson Research Center 3/17/2015 2014 IBM Corporation Outline IBM OpenPower Platform Accelerating