If you installed VM and Linux libraries as in the tutorial, you should not get any errors. Otherwise, you may need to install wget or gunzip.
|
|
- Chad Giles Gibbs
- 6 years ago
- Views:
Transcription
1 MNIST 1- Prepare Dataset cd $CAFFE_ROOT./data/mnist/get_mnist.sh./examples/mnist/create_mnist.sh If you installed VM and Linux libraries as in the tutorial, you should not get any errors. Otherwise, you may need to install wget or gunzip. 2-Open Text Editor cd $CAFFE_ROOT cd examples cd mnist gedit Now, you have a text editor that waits for you to configure the deep architecture. 3-Define Network Name name: "LeNet" 3-Define Trian Data Layer name: "mnist" type: "Data" top: "data" top: "label" include { phase: TRAIN transform_ scale: data_ source: "examples/mnist/mnist_train_lmdb" batch_size: 64 backend: LMDB
2 Let s look at what the code refers. mnist is the layer name. DATA is the layer type. Data is read from the lmdb source. Batch size is 64. Scale is set to 1/256 to set the pixel value range to [0,1). This layers produces two blobs as data and label. The naming is self-explanatory so the layer definitions can easily be understood. 3-Define Test Data Layer name: "mnist" type: "Data" top: "data" top: "label" include { phase: TEST transform_ scale: data_ source: "examples/mnist/mnist_test_lmdb" batch_size: 100 backend: LMDB 4 Define Convolutional Layer name: "conv1" type: "Convolution" bottom: "data" top: "conv1" lr_mult: 1 lr_mult: 2 convolution_ num_output: 20 kernel_size: 5 stride: 1 weight_filler { type: "xavier" bias_filler { type: "constant"
3 This layer takes the data blob as input and generates conv1 layer. Output has 20 channels, kernel size is set to 5 and stride is 1. Weights and bias values are randomly initialized. xavier is the algorithm to adjust the scale of the initialization based on number of input and output neurons. lr_mult are the learning rate adjustments. It means the weight learning rate is set to the same value given by the solver and bias learning rate is set to the double. 5- Define Pooling Layer name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_ pool: MAX kernel_size: 2 stride: 2 We defined a non-overlapping max pooling operation with block size and stride of 2. Let s add another convolutional and pooling layer to increase the abstraction in the network. 6- Define another Pooling and Convolutional Layer name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" lr_mult: 1 lr_mult: 2 convolution_ num_output: 50 kernel_size: 5 stride: 1 weight_filler { type: "xavier" bias_filler { type: "constant" name: "pool2" type: "Pooling" bottom: "conv2"
4 top: "pool2" pooling_ pool: MAX kernel_size: 2 stride: 2 7 Define the Fully Connected Layer name: "ip1" type: "InnerProduct" bottom: "pool2" top: "ip1" lr_mult: 1 lr_mult: 2 inner_product_ num_output: 500 weight_filler { type: "xavier" bias_filler { type: "constant" This layers take the input from the pooling layer and outputs 500 nodes. 8- Define the Activation Layer name: "relu1" type: "ReLU" bottom: "ip1" top: "ip1" Note that the bottom and top layers are defined as the same. This kind of configuration corresponds to the in-place operation which can be used for element-wise operations to save some memory. 8- Define another Fully Connected Layer
5 name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" lr_mult: 1 lr_mult: 2 inner_product_ num_output: 10 weight_filler { type: "xavier" bias_filler { type: "constant" 9- Define the Accuracy layer name: "accuracy" type: "Accuracy" bottom: "ip2" bottom: "label" top: "accuracy" include { phase: TEST This layer is just to show the accuracy of the output with respect to the target and it does not have a backward step Define the Loss Layer name: "loss" type: "SoftmaxWithLoss" bottom: "ip2" bottom: "label" top: "loss" - Save the file as msl_lenet_train_test.prototxt
6 Loss layer takes the predictions and labels as the input. This layer does not have any outputs but it initiates the gradient and calculates the loss when backpropagation starts. 11 Define Solver - Go to $CAFFE_ROOT/examples/mnist - Open text editor and type the following: # The train/test net protocol buffer definition net: "examples/mnist/lenet_train_test.prototxt" # test_iter specifies how many forward passes the test should carry out. # In the case of MNIST, we have test batch size 100 and 100 test iterations, # covering the full 10,000 testing images. test_iter: 100 # Carry out testing every 500 training iterations. test_interval: 500 # The base learning rate, momentum and the weight decay of the network. base_lr: 0.01 momentum: 0.9 weight_decay: # The learning rate policy lr_policy: "inv" gamma: power: 0.75 # Display every 100 iterations display: 100 # The maximum number of iterations max_iter: # snapshot intermediate results snapshot: 5000 snapshot_prefix: "examples/mnist/lenet" # solver mode: CPU or GPU solver_mode: CPU - Save the file as msl_lenet_solver.prototxt: 12 - Write the test script - Go to $CAFFE_ROOT/examples/mnist - Open text editor and type the following: #!/usr/bin/env sh./build/tools/caffe train --solver=examples/mnist/msl_lenet_solver.prototxt - Save the file as msl_lenet.sh - Go to $CAFFE_ROOT/examples/mnist chmod +x msl_lenet.sh
7 13 Run the test script cd $CAFFE_ROOT./examples/mnist/msl_lenet.sh Layer Writing Rules: layers { //...layer definition... include: { phase: TRAIN Layers can have rules about when and how they are included in the network. For example, if the layer definition includes the above statement, that layer is only included in the training phase. Layer Types in Caffe Vision Layers [KEYWORD] PS: Keywords can change from version to the version - Convolution [CONVOLUTION] - Pooling [POOLING] - Local Response Normalization [LRN] Loss Layers - Softmax [SOFTMAX_LOSS] - Sum-of-Squares / Euclidean [EUCLIDEAN_LOSS] - Hinge/Margin [HINGE_LOSS] - Sigmoid Cross Entropy [SIGMOID_CROSS_ENTROPY_LOSS] - Infogain [INFOGAIN_LOSS] - Accuracy and Top-k: [ACCURACY]: accuracy of the output with respect to the target, no backward steps Activation / Neuron Layers
8 - ReLU / Rectifies-Linear and Leaky-ReLU [RELU] - Sigmoid [SIGMOID] - TanH / Hyperbolic Tangent [TANH] - Absolute Value [ABSVAL] - Power [POWER] - Binomial Normal Log Likelihood [BNLL] Data Layers - Database [DATA] - Memory [In-Memory]: Reads data directly from memory without copying it - HDF5 Output [HDF5_OUTPUT]: Write input blobs to disk - Images [IMAGE_DATA] - Windows [WINDOWS_DATA] - Dummy [DUMMY_DATA] Common Layers - Inner Product [INNER_PRODUCT] - Splitting [SPLIT]: input blob -> multiple output blobs - Flattening [FLATTEN]: Blob to vector conversion - Concatenation [CONCAT] - Slicing [SLICE]: input layer -> multiple output layer - Element-wise operations [ELTWISE] - Argmax [ARGMAX] - Softmax [SOFTMAX] - Mean-Variance Normalization [MVN]
Deep learning using Caffe Execution Process
Deep learning using Caffe Execution Process Tassadaq Hussain Riphah International University Barcelona Supercomputing Center UCERD Pvt Ltd Open source deep learning packages Caffe C++/CUDA based. MATLAB/python
More informationAccelerating Convolutional Neural Nets. Yunming Zhang
Accelerating Convolutional Neural Nets Yunming Zhang Focus Convolutional Neural Nets is the state of the art in classifying the images The models take days to train Difficult for the programmers to tune
More informationCAFFE TUTORIAL ROHIT GIRDHAR. Brewing Deep Networks With Caffe. Many slides from Xinlei Chen ( tutorial), Caffe CVPR 15 tutorial
CAFFE TUTORIAL Brewing Deep Networks With Caffe ROHIT GIRDHAR Many slides from Xinlei Chen (16-824 tutorial), Caffe CVPR 15 tutorial Overview Motivation and comparisons Training/Finetuning a simple model
More informationCAFFE TUTORIAL. Brewing Deep Networks With Caffe XINLEI CHEN
CAFFE TUTORIAL Brewing Deep Networks With Caffe XINLEI CHEN ! this->tutorial What is Deep Learning? Why Deep Learning? The Unreasonable Effectiveness of Deep Features History of Deep Learning. CNNs 1989
More informationMachine Learning Workshop
Machine Learning Workshop {Presenters} Feb. 20th, 2018 Theory of Neural Networks Architecture and Types of Layers: Fully Connected (FC) Convolutional Neural Network (CNN) Pooling Drop out Residual Recurrent
More informationImage Classification using Transfer Learning from Siamese Networks based on Text Metadata Similarity
Image Classification using Transfer Learning from Siamese Networks based on Text Metadata Similarity Dan Iter Stanford University daniter@stanford.edu Abstract Convolutional neural networks learn about
More informationGetting started with Caffe. Jon Barker, Solutions Architect
Getting started with Caffe Jon Barker, Solutions Architect Caffe tour Overview Agenda Example applications Setup Performance Hands-on lab preview 2 A tour of Caffe 3 What is Caffe? An open framework for
More informationDeep 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
More informationDay 1 Lecture 6. Software Frameworks for Deep Learning
Day 1 Lecture 6 Software Frameworks for Deep Learning Packages Caffe Theano NVIDIA Digits Lasagne Keras Blocks Torch TensorFlow MxNet MatConvNet Nervana Neon Leaf Caffe Deep learning framework from Berkeley
More informationNVCAFFE. DU _v April User Guide
NVCAFFE DU-08517-001_v0.16.5 April 2018 User Guide TABLE OF CONTENTS Chapter 1. Overview Of... 1 1.1. Contents Of The Container...1 Chapter 2. Pulling An Container... 2 Chapter 3. Verifying... 3 Chapter
More informationIntroduction to Neural Networks and Brief Tutorial with Caffe 10 th Set of Notes
Introduction to Neural Networks and Brief Tutorial with Caffe 10 th Set of Notes Assembled by Qilin Zhang, based on [NNDL], [DLT], [Caffe], etc. Notes for the CS 559 Machine Learning Class Outline Neural
More informationDeep 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
More informationPyramidal Deep Models for Computer Vision
Pyramidal Deep Models for Computer Vision Alfredo PETROSINO* and Ihsan ULLAH** *Computer Vision and Pattern Recognition (CVPR) Lab University of Naples Parthenope, Department of Science and Technology
More informationIndex. Umberto Michelucci 2018 U. Michelucci, Applied Deep Learning,
A Acquisition function, 298, 301 Adam optimizer, 175 178 Anaconda navigator conda command, 3 Create button, 5 download and install, 1 installing packages, 8 Jupyter Notebook, 11 13 left navigation pane,
More informationDeep 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
More informationMachine 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
More informationNVIDIA 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
More informationA Quick Guide on Training a neural network using Keras.
A Quick Guide on Training a neural network using Keras. TensorFlow and Keras Keras Open source High level, less flexible Easy to learn Perfect for quick implementations Starts by François Chollet from
More informationCaffe tutorial. Seong Joon Oh
Caffe tutorial Seong Joon Oh What is Caffe? Convolution Architecture For Feature Extraction (CAFFE) Open framework, models, and examples for deep learning 600+ citations, 100+ contributors, 7,000+ stars,
More informationRNN LSTM and Deep Learning Libraries
RNN LSTM and Deep Learning Libraries UDRC Summer School Muhammad Awais m.a.rana@surrey.ac.uk Outline Recurrent Neural Network Application of RNN LSTM Caffe Torch Theano TensorFlow Flexibility of Recurrent
More informationCNN Basics. Chongruo Wu
CNN Basics Chongruo Wu Overview 1. 2. 3. Forward: compute the output of each layer Back propagation: compute gradient Updating: update the parameters with computed gradient Agenda 1. Forward Conv, Fully
More informationSEMANTIC COMPUTING. Lecture 8: Introduction to Deep Learning. TU Dresden, 7 December Dagmar Gromann International Center For Computational Logic
SEMANTIC COMPUTING Lecture 8: Introduction to Deep Learning Dagmar Gromann International Center For Computational Logic TU Dresden, 7 December 2018 Overview Introduction Deep Learning General Neural Networks
More informationCOMP9444 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
More informationCIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm
CIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm Instructions CNNs is a team project. The maximum size of a team
More informationKeras: Handwritten Digit Recognition using MNIST Dataset
Keras: Handwritten Digit Recognition using MNIST Dataset IIT PATNA January 31, 2018 1 / 30 OUTLINE 1 Keras: Introduction 2 Installing Keras 3 Keras: Building, Testing, Improving A Simple Network 2 / 30
More informationKeras: Handwritten Digit Recognition using MNIST Dataset
Keras: Handwritten Digit Recognition using MNIST Dataset IIT PATNA February 9, 2017 1 / 24 OUTLINE 1 Introduction Keras: Deep Learning library for Theano and TensorFlow 2 Installing Keras Installation
More informationMachine Learning 13. week
Machine Learning 13. week Deep Learning Convolutional Neural Network Recurrent Neural Network 1 Why Deep Learning is so Popular? 1. Increase in the amount of data Thanks to the Internet, huge amount of
More informationMoonRiver: 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
More informationAll 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
More informationCS 6501: Deep Learning for Computer Graphics. Training Neural Networks II. Connelly Barnes
CS 6501: Deep Learning for Computer Graphics Training Neural Networks II Connelly Barnes Overview Preprocessing Initialization Vanishing/exploding gradients problem Batch normalization Dropout Additional
More informationManaging Caffe Deep Learning with HTCondor
Managing Caffe Deep Learning with HTCondor Integrated Defense Systems Michael V. Pelletier, Principal Engineer May 2018 Approved under etpcr IDS-14060 Copyright 2018, Raytheon Company. All rights reserved.
More informationIMPLEMENTING DEEP LEARNING USING CUDNN 이예하 VUNO INC.
IMPLEMENTING DEEP LEARNING USING CUDNN 이예하 VUNO INC. CONTENTS Deep Learning Review Implementation on GPU using cudnn Optimization Issues Introduction to VUNO-Net DEEP LEARNING REVIEW BRIEF HISTORY OF NEURAL
More informationDeep Learning Cook Book
Deep Learning Cook Book Robert Haschke (CITEC) Overview Input Representation Output Layer + Cost Function Hidden Layer Units Initialization Regularization Input representation Choose an input representation
More informationPerceptron: 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
More informationFuzzy Set Theory in Computer Vision: Example 3, Part II
Fuzzy Set Theory in Computer Vision: Example 3, Part II Derek T. Anderson and James M. Keller FUZZ-IEEE, July 2017 Overview Resource; CS231n: Convolutional Neural Networks for Visual Recognition https://github.com/tuanavu/stanford-
More informationMachine Learning and Algorithms for Data Mining Practical 2: Neural Networks
CST Part III/MPhil in Advanced Computer Science 2016-2017 Machine Learning and Algorithms for Data Mining Practical 2: Neural Networks Demonstrators: Petar Veličković, Duo Wang Lecturers: Mateja Jamnik,
More informationCPSC 340: Machine Learning and Data Mining. Deep Learning Fall 2016
CPSC 340: Machine Learning and Data Mining Deep Learning Fall 2016 Assignment 5: Due Friday. Assignment 6: Due next Friday. Final: Admin December 12 (8:30am HEBB 100) Covers Assignments 1-6. Final from
More informationLecture 20: Neural Networks for NLP. Zubin Pahuja
Lecture 20: Neural Networks for NLP Zubin Pahuja zpahuja2@illinois.edu courses.engr.illinois.edu/cs447 CS447: Natural Language Processing 1 Today s Lecture Feed-forward neural networks as classifiers simple
More informationLecture : Neural net: initialization, activations, normalizations and other practical details Anne Solberg March 10, 2017
INF 5860 Machine learning for image classification Lecture : Neural net: initialization, activations, normalizations and other practical details Anne Solberg March 0, 207 Mandatory exercise Available tonight,
More informationTutorial on Machine Learning Tools
Tutorial on Machine Learning Tools Yanbing Xue Milos Hauskrecht Why do we need these tools? Widely deployed classical models No need to code from scratch Easy-to-use GUI Outline Matlab Apps Weka 3 UI TensorFlow
More informationImproving the way neural networks learn Srikumar Ramalingam School of Computing University of Utah
Improving the way neural networks learn Srikumar Ramalingam School of Computing University of Utah Reference Most of the slides are taken from the third chapter of the online book by Michael Nielson: neuralnetworksanddeeplearning.com
More informationMocha.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:
More informationDynamic Routing Between Capsules
Report Explainable Machine Learning Dynamic Routing Between Capsules Author: Michael Dorkenwald Supervisor: Dr. Ullrich Köthe 28. Juni 2018 Inhaltsverzeichnis 1 Introduction 2 2 Motivation 2 3 CapusleNet
More informationNatural Language Processing CS 6320 Lecture 6 Neural Language Models. Instructor: Sanda Harabagiu
Natural Language Processing CS 6320 Lecture 6 Neural Language Models Instructor: Sanda Harabagiu In this lecture We shall cover: Deep Neural Models for Natural Language Processing Introduce Feed Forward
More informationDeep neural networks II
Deep neural networks II May 31 st, 2018 Yong Jae Lee UC Davis Many slides from Rob Fergus, Svetlana Lazebnik, Jia-Bin Huang, Derek Hoiem, Adriana Kovashka, Why (convolutional) neural networks? State of
More informationSupervised Learning in Neural Networks (Part 2)
Supervised Learning in Neural Networks (Part 2) Multilayer neural networks (back-propagation training algorithm) The input signals are propagated in a forward direction on a layer-bylayer basis. Learning
More informationMultinomial Regression and the Softmax Activation Function. Gary Cottrell!
Multinomial Regression and the Softmax Activation Function Gary Cottrell Notation reminder We have N data points, or patterns, in the training set, with the pattern number as a superscript: {(x 1,t 1 ),
More informationSurvey of Convolutional Neural Network
Survey of Convolutional Neural Network Chenyou Fan Indiana University Bloomington, IN fan6@indiana.edu Abstract Convolutional Neural Network (CNN) was firstly introduced in Computer Vision for image recognition
More informationTutorial 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
More informationMachine 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
More informationIntro 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
More informationFusion of Mini-Deep Nets
Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 5-2016 Fusion of Mini-Deep Nets Sai Prasad Nooka spn8235@rit.edu Follow this and additional works at: http://scholarworks.rit.edu/theses
More informationDeep 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
More informationConvolutional 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:
More informationDeep Learning. Vladimir Golkov Technical University of Munich Computer Vision Group
Deep Learning Vladimir Golkov Technical University of Munich Computer Vision Group 1D Input, 1D Output target input 2 2D Input, 1D Output: Data Distribution Complexity Imagine many dimensions (data occupies
More informationDeep Learning. Practical introduction with Keras JORDI TORRES 27/05/2018. Chapter 3 JORDI TORRES
Deep Learning Practical introduction with Keras Chapter 3 27/05/2018 Neuron A neural network is formed by neurons connected to each other; in turn, each connection of one neural network is associated
More informationLecture : Training a neural net part I Initialization, activations, normalizations and other practical details Anne Solberg February 28, 2018
INF 5860 Machine learning for image classification Lecture : Training a neural net part I Initialization, activations, normalizations and other practical details Anne Solberg February 28, 2018 Reading
More informationNeural Network Compiler BNN Scripts User Guide
FPGA-UG-02055 Version 1.0 May 2018 Contents 1. Introduction... 3 2. Software Requirements... 3 3. Directory Structure... 3 4. Installation Guide... 4 4.1. Installing Dependencies... 4 4.2. Installing Packages...
More informationDistributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability Janis Keuper Itwm.fraunhofer.de/ml Competence Center High Performance Computing Fraunhofer ITWM, Kaiserslautern,
More informationHello Edge: Keyword Spotting on Microcontrollers
Hello Edge: Keyword Spotting on Microcontrollers Yundong Zhang, Naveen Suda, Liangzhen Lai and Vikas Chandra ARM Research, Stanford University arxiv.org, 2017 Presented by Mohammad Mofrad University of
More informationAdvanced Machine Learning
Advanced Machine Learning Convolutional Neural Networks for Handwritten Digit Recognition Andreas Georgopoulos CID: 01281486 Abstract Abstract At this project three different Convolutional Neural Netwroks
More informationCUDNN. DU _v07 May Developer Guide
CUDNN DU-06702-001_v07 May 2018 Developer Guide TABLE OF CONTENTS Chapter 1. Overview... 1 Chapter 2. General Description... 2 2.1. Programming Model...2 2.2. Notation... 2 2.3. Tensor Descriptor... 3
More informationImageNet 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
More informationLecture 2 Notes. Outline. Neural Networks. The Big Idea. Architecture. Instructors: Parth Shah, Riju Pahwa
Instructors: Parth Shah, Riju Pahwa Lecture 2 Notes Outline 1. Neural Networks The Big Idea Architecture SGD and Backpropagation 2. Convolutional Neural Networks Intuition Architecture 3. Recurrent Neural
More informationGlobal Optimality in Neural Network Training
Global Optimality in Neural Network Training Benjamin D. Haeffele and René Vidal Johns Hopkins University, Center for Imaging Science. Baltimore, USA Questions in Deep Learning Architecture Design Optimization
More informationDeep Learning. Volker Tresp Summer 2014
Deep Learning Volker Tresp Summer 2014 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
More informationMulti-Task Self-Supervised Visual Learning
Multi-Task Self-Supervised Visual Learning Sikai Zhong March 4, 2018 COMPUTER SCIENCE Table of contents 1. Introduction 2. Self-supervised Tasks 3. Architectures 4. Experiments 1 Introduction Self-supervised
More informationCNN Visualizations. Seoul AI Meetup Martin Kersner, 2018/01/06
CNN Visualizations Seoul AI Meetup Martin Kersner, 2018/01/06 Content 1. Visualization of convolutional weights from the first layer 2. Visualization of patterns learned by higher layers 3. Weakly Supervised
More informationReverse Engineering AI Models
Reverse Engineering AI Models Kang Li kangli.ctf@gmail.com Collaborators:Deyue Zhang, Jiayu Qian, Yufei Chen About Me Professor of Computer Science at UGA Founder of the SecDawgs, Disekt CTF Teams Founding
More informationINTRODUCTION 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
More informationAdditive Manufacturing Defect Detection using Neural Networks
Additive Manufacturing Defect Detection using Neural Networks James Ferguson Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Knoxville, Tennessee 37996 Jfergu35@vols.utk.edu
More informationDeep Neural Networks Optimization
Deep Neural Networks Optimization Creative Commons (cc) by Akritasa http://arxiv.org/pdf/1406.2572.pdf Slides from Geoffrey Hinton CSC411/2515: Machine Learning and Data Mining, Winter 2018 Michael Guerzhoy
More informationIntroduction to Neural Networks
Introduction to Neural Networks Jakob Verbeek 2017-2018 Biological motivation Neuron is basic computational unit of the brain about 10^11 neurons in human brain Simplified neuron model as linear threshold
More informationConvolutional Networks in Scene Labelling
Convolutional Networks in Scene Labelling Ashwin Paranjape Stanford ashwinpp@stanford.edu Ayesha Mudassir Stanford aysh@stanford.edu Abstract This project tries to address a well known problem of multi-class
More informationCIS680: Vision & Learning Assignment 2.b: RPN, Faster R-CNN and Mask R-CNN Due: Nov. 21, 2018 at 11:59 pm
CIS680: Vision & Learning Assignment 2.b: RPN, Faster R-CNN and Mask R-CNN Due: Nov. 21, 2018 at 11:59 pm Instructions This is an individual assignment. Individual means each student must hand in their
More informationDeep Learning For Video Classification. Presented by Natalie Carlebach & Gil Sharon
Deep Learning For Video Classification Presented by Natalie Carlebach & Gil Sharon Overview Of Presentation Motivation Challenges of video classification Common datasets 4 different methods presented in
More informationCode 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
More informationMulti-layer Perceptron Forward Pass Backpropagation. Lecture 11: Aykut Erdem November 2016 Hacettepe University
Multi-layer Perceptron Forward Pass Backpropagation Lecture 11: Aykut Erdem November 2016 Hacettepe University Administrative Assignment 2 due Nov. 10, 2016! Midterm exam on Monday, Nov. 14, 2016 You are
More informationDeep Learning Explained Module 4: Convolution Neural Networks (CNN or Conv Nets)
Deep Learning Explained Module 4: Convolution Neural Networks (CNN or Conv Nets) Sayan D. Pathak, Ph.D., Principal ML Scientist, Microsoft Roland Fernandez, Senior Researcher, Microsoft Module Outline
More informationKnow 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
More informationInception and Residual Networks. Hantao Zhang. Deep Learning with Python.
Inception and Residual Networks Hantao Zhang Deep Learning with Python https://en.wikipedia.org/wiki/residual_neural_network Deep Neural Network Progress from Large Scale Visual Recognition Challenge (ILSVRC)
More informationConvolutional Neural Networks and Supervised Learning
Convolutional Neural Networks and Supervised Learning Eilif Solberg August 30, 2018 Outline Convolutional Architectures Convolutional neural networks Training Loss Optimization Regularization Hyperparameter
More informationConvolutional 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
More informationDeep 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
More informationResearch on Pruning Convolutional Neural Network, Autoencoder and Capsule Network
Research on Pruning Convolutional Neural Network, Autoencoder and Capsule Network Tianyu Wang Australia National University, Colledge of Engineering and Computer Science u@anu.edu.au Abstract. Some tasks,
More informationCSE 559A: Computer Vision
CSE 559A: Computer Vision Fall 2018: T-R: 11:30-1pm @ Lopata 101 Instructor: Ayan Chakrabarti (ayan@wustl.edu). Course Staff: Zhihao Xia, Charlie Wu, Han Liu http://www.cse.wustl.edu/~ayan/courses/cse559a/
More informationCMU Lecture 18: Deep learning and Vision: Convolutional neural networks. Teacher: Gianni A. Di Caro
CMU 15-781 Lecture 18: Deep learning and Vision: Convolutional neural networks Teacher: Gianni A. Di Caro DEEP, SHALLOW, CONNECTED, SPARSE? Fully connected multi-layer feed-forward perceptrons: More powerful
More informationPredicting Goal-Scoring Opportunities in Soccer by Using Deep Convolutional Neural Networks. Master s Thesis
Predicting Goal-Scoring Opportunities in Soccer by Using Deep Convolutional Neural Networks Martijn Wagenaar 6 November 26 Master s Thesis Department of Artificial Intelligence, University of Groningen,
More informationCS 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
More informationConvolutional 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.
More informationIntroduction to Deep Learning
ENEE698A : Machine Learning Seminar Introduction to Deep Learning Raviteja Vemulapalli Image credit: [LeCun 1998] Resources Unsupervised feature learning and deep learning (UFLDL) tutorial (http://ufldl.stanford.edu/wiki/index.php/ufldl_tutorial)
More informationCS230: Deep Learning Winter Quarter 2018 Stanford University
: Deep Learning Winter Quarter 08 Stanford University Midterm Examination 80 minutes Problem Full Points Your Score Multiple Choice 7 Short Answers 3 Coding 7 4 Backpropagation 5 Universal Approximation
More informationConvolutional Neural Network Layer Reordering for Acceleration
R1-15 SASIMI 2016 Proceedings Convolutional Neural Network Layer Reordering for Acceleration Vijay Daultani Subhajit Chaudhury Kazuhisa Ishizaka System Platform Labs Value Co-creation Center System Platform
More informationEverything you wanted to know about Deep Learning for Computer Vision but were afraid to ask
Everything you wanted to know about Deep Learning for Computer Vision but were afraid to ask Moacir A. Ponti, Leonardo S. F. Ribeiro, Tiago S. Nazare ICMC University of São Paulo São Carlos/SP, 13566-590,
More informationarxiv: v3 [cs.lg] 30 Dec 2016
Video Ladder Networks Francesco Cricri Nokia Technologies francesco.cricri@nokia.com Xingyang Ni Tampere University of Technology xingyang.ni@tut.fi arxiv:1612.01756v3 [cs.lg] 30 Dec 2016 Mikko Honkala
More informationDeep Convolutional Neural Networks. Nov. 20th, 2015 Bruce Draper
Deep Convolutional Neural Networks Nov. 20th, 2015 Bruce Draper Background: Fully-connected single layer neural networks Feed-forward classification Trained through back-propagation Example Computer Vision
More informationReal-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor Supplemental Document
Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor Supplemental Document Franziska Mueller 1,2 Dushyant Mehta 1,2 Oleksandr Sotnychenko 1 Srinath Sridhar 1 Dan Casas 3 Christian Theobalt
More informationComo 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
More informationCENG 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
More informationCS231N Course Project Report Classifying Shadowgraph Images of Planktons Using Convolutional Neural Networks
CS231N Course Project Report Classifying Shadowgraph Images of Planktons Using Convolutional Neural Networks Shane Soh Stanford University shanesoh@stanford.edu 1. Introduction In this final project report,
More information