Machine Learning: Chenhao Tan University of Colorado Boulder LECTURE 15
|
|
- Ethan Lane
- 5 years ago
- Views:
Transcription
1 Machine Learning: Chenhao Tan University of Colorado Boulder LECTURE 15 Slides adapted from Jordan Boyd-Graber Machine Learning: Chenhao Tan Boulder 1 of 21
2 Logistics HW3 available on Github, due on October 19 First prelim next Monday Final project team formation due by October 12 Machine Learning: Chenhao Tan Boulder 2 of 21
3 Recap Outline Recap Machine Learning: Chenhao Tan Boulder 3 of 21
4 Recap Logistic Regression as a Single-layer Neural Network Input layer x 1 x 2... Single Layer o 1... What is the activation used in logistic regression? What is the objective function used in logistic regression? x d Machine Learning: Chenhao Tan Boulder 4 of 21
5 Recap Nonlinearity Options Sigmoid tanh ReLU (rectified linear unit) f (x) = f (x) = exp(x) exp(x) exp( x) exp(x) + exp( x) f (x) = max(0, x) softmax x = exp(x) x i exp(x i ) Machine Learning: Chenhao Tan Boulder 5 of 21
6 Recap Nonlinearity Options Machine Learning: Chenhao Tan Boulder 6 of 21
7 Recap Loss Function Options l 2 loss l 1 loss Cross entropy (logistic regression) (y i ŷ i ) 2 i y i ŷ i i i y i log ŷ i Hinge loss (more on this during SVM) max(0, 1 yŷ) Machine Learning: Chenhao Tan Boulder 7 of 21
8 Recap Forward propagation algorithm How do we make predictions based on a multi-layer neural network? Store the biases for layer l in b l, weight matrix in W l W 1, b 1 W 2, b 2 W 3, b 3 W 4, b 4 x 1 x 2 o x d Machine Learning: Chenhao Tan Boulder 8 of 21
9 Recap Forward propagation algorithm Suppose your network has L layers Make prediction for an instance x 1: Initialize a 0 = x 2: for l = 1 to L do 3: z l = W l a l 1 + b l 4: a l = g(z l ) // g represents the nonlinear activation 5: end for 6: The prediction ŷ is simply a L Machine Learning: Chenhao Tan Boulder 9 of 21
10 Recap Quiz 1 How many parameters are there in the following feed-forward neural networks (assuming no biases)? x 1 x 2... x 100 h 1... h 50 A. 100 * * * 5 h 1... h 20 B. 100 * * * o 1... o 5 Machine Learning: Chenhao Tan Boulder 10 of 21
11 Recap Neural networks in a nutshell Training data S train = {(x, y)} Network architecture (model) ŷ = f w (x) Loss function (objective function) L (y, ŷ) Learning (next week) Machine Learning: Chenhao Tan Boulder 11 of 21
12 Recap Quiz 2 Which of the following statements is true? (Suppose that training data is large.) A. In training, K-nearest neighbors takes shorter time than neural networks. B. In training, K-nearest neighbors takes longer time than neural networks. C. In testing, K-nearest neighbors takes shorter time than neural networks. D. In testing, K-nearest neighbors takes longer time than neural networks. Machine Learning: Chenhao Tan Boulder 12 of 21
13 Outline Recap Machine Learning: Chenhao Tan Boulder 13 of 21
14 Structured data Spatial information after_she_was_told_she_was_a_good_girl/ Machine Learning: Chenhao Tan Boulder 14 of 21
15 Convolutional Layers Sharing parameters across patches input image or input feature map output feature maps a i j = k k w ij x ij i=1 j=1 Number of filters Filter shape Stride size tikz-convolutional-layer/convolutional-layer.tex Machine Learning: Chenhao Tan Boulder 15 of 21
16 Quiz 3 A concrete example (assuming no biases, convolution with 4 filters, ReLU, max pooling, and finally a fully-connected softmax layer) input image (10*10) 4@6*6 4@3*3 5*1 4 filters 5*5, stride 1 Max pooling 2*2, stride 2 Machine Learning: Chenhao Tan Boulder 16 of 21
17 Quiz 3 How many parameters are there in the following convolutional neural networks? (assuming no biases, convolution with 4 filters, ReLU, max pooling, and finally a fully-connected softmax layer) input image (10*10) 4@6*6 4@3*3 5*1 4 filters 5*5, stride 1 Max pooling 2*2, stride 2 Machine Learning: Chenhao Tan Boulder 16 of 21
18 Quiz 3 How many ReLU operations are performed on the forward pass? (assuming no biases, convolution with 4 filters, ReLU, max pooling, and finally a fully-connected softmax layer) input image (10*10) 4@6*6 4@3*3 5*1 4 filters 5*5, stride 1 Max pooling 2*2, stride 2 Machine Learning: Chenhao Tan Boulder 16 of 21
19 Structured data Sequential information My words fly up, my thoughts remain below: Words without thoughts never to heaven go. Hamlet Machine Learning: Chenhao Tan Boulder 17 of 21
20 Structured data Sequential information My words fly up, my thoughts remain below: Words without thoughts never to heaven go. Hamlet language activity history Machine Learning: Chenhao Tan Boulder 17 of 21
21 Structured data Sequential information My words fly up, my thoughts remain below: Words without thoughts never to heaven go. Hamlet language activity history x = (x 1,..., x T ) Machine Learning: Chenhao Tan Boulder 17 of 21
22 Recurrent Layers Sharing parameters along a sequence h t = f (x t, h t 1 ) Machine Learning: Chenhao Tan Boulder 18 of 21
23 Recurrent Layers Sharing parameters along a sequence h t = f (x t, h t 1 ) Machine Learning: Chenhao Tan Boulder 18 of 21
24 Long short-term memory A commonly used recurrent neural network in natural language processing f t = σ(w f [h t 1, x t ] + b f ) i t = σ(w i [h t 1, x t ] + b i ) o t = σ(w o [h t 1, x t ] + b o ) C t = tanh(w C [h t 1, x t ] + b C ) C t = f t C t 1 + i t C t h t = o t tanh(c t ) Machine Learning: Chenhao Tan Boulder 19 of 21
25 Wrap up Neural networks Network architecture (a lot more then fully connected layers) Convulutional layer Recurrent layer Loss function Machine Learning: Chenhao Tan Boulder 20 of 21
26 What is missing? How to find good weights? How to make the model work (regularization, even more architecture, etc)? Machine Learning: Chenhao Tan Boulder 21 of 21
Machine 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 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 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 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 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 Applications
October 20, 2017 Overview Supervised Learning Feedforward neural network Convolution neural network Recurrent neural network Recursive neural network (Recursive neural tensor network) Unsupervised Learning
More informationObject 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
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 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 informationHidden Units. Sargur N. Srihari
Hidden Units Sargur N. srihari@cedar.buffalo.edu 1 Topics in Deep Feedforward Networks Overview 1. Example: Learning XOR 2. Gradient-Based Learning 3. Hidden Units 4. Architecture Design 5. Backpropagation
More informationMachine 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
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 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 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 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 informationMidterm Review. CS230 Fall 2018
Midterm Review CS23 Fall 28 Broadcasting Calculating Means How would you calculate the means across the rows of the following matrix? How about the columns? Calculating Means How would you calculate the
More informationPractical Methodology. Lecture slides for Chapter 11 of Deep Learning Ian Goodfellow
Practical Methodology Lecture slides for Chapter 11 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-26 What drives success in ML? Arcane knowledge of dozens of obscure algorithms? Mountains
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 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 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 informationCSC 578 Neural Networks and Deep Learning
CSC 578 Neural Networks and Deep Learning Fall 2018/19 7. Recurrent Neural Networks (Some figures adapted from NNDL book) 1 Recurrent Neural Networks 1. Recurrent Neural Networks (RNNs) 2. RNN Training
More informationKnowledge Discovery and Data Mining. Neural Nets. A simple NN as a Mathematical Formula. Notes. Lecture 13 - Neural Nets. Tom Kelsey.
Knowledge Discovery and Data Mining Lecture 13 - Neural Nets Tom Kelsey School of Computer Science University of St Andrews http://tom.home.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-13-NN
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 informationKnowledge Discovery and Data Mining
Knowledge Discovery and Data Mining Lecture 13 - Neural Nets Tom Kelsey School of Computer Science University of St Andrews http://tom.home.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-13-NN
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 informationPixel-level Generative Model
Pixel-level Generative Model Generative Image Modeling Using Spatial LSTMs (2015NIPS) L. Theis and M. Bethge University of Tübingen, Germany Pixel Recurrent Neural Networks (2016ICML) A. van den Oord,
More informationAdministrative. Assignment 1 due Wednesday April 18, 11:59pm
Lecture 4-1 Administrative Assignment 1 due Wednesday April 18, 11:59pm Lecture 4-2 Administrative All office hours this week will use queuestatus Lecture 4-3 Where we are... scores function SVM loss data
More informationDeep Learning. Deep Learning provided breakthrough results in speech recognition and image classification. Why?
Data Mining Deep Learning Deep Learning provided breakthrough results in speech recognition and image classification. Why? Because Speech recognition and image classification are two basic examples of
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 informationHomework 5. Due: April 20, 2018 at 7:00PM
Homework 5 Due: April 20, 2018 at 7:00PM Written Questions Problem 1 (25 points) Recall that linear regression considers hypotheses that are linear functions of their inputs, h w (x) = w, x. In lecture,
More information16-785: Integrated Intelligence in Robotics: Vision, Language, and Planning. Spring 2018 Lecture 14. Image to Text
16-785: Integrated Intelligence in Robotics: Vision, Language, and Planning Spring 2018 Lecture 14. Image to Text Input Output Classification tasks 4/1/18 CMU 16-785: Integrated Intelligence in Robotics
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 informationNatural Language Processing with Deep Learning CS224N/Ling284. Christopher Manning Lecture 4: Backpropagation and computation graphs
Natural Language Processing with Deep Learning CS4N/Ling84 Christopher Manning Lecture 4: Backpropagation and computation graphs Lecture Plan Lecture 4: Backpropagation and computation graphs 1. Matrix
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 informationInstance-based Learning
Instance-based Learning Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University October 15 th, 2007 2005-2007 Carlos Guestrin 1 1-Nearest Neighbor Four things make a memory based learner:
More informationSequence Modeling: Recurrent and Recursive Nets. By Pyry Takala 14 Oct 2015
Sequence Modeling: Recurrent and Recursive Nets By Pyry Takala 14 Oct 2015 Agenda Why Recurrent neural networks? Anatomy and basic training of an RNN (10.2, 10.2.1) Properties of RNNs (10.2.2, 8.2.6) Using
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 informationCIS 660. Image Searching System using CNN-LSTM. Presented by. Mayur Rumalwala Sagar Dahiwala
CIS 660 using CNN-LSTM Presented by Mayur Rumalwala Sagar Dahiwala AGENDA Problem in Image Searching? Proposed Solution Tools, Library and Dataset used Architecture of Proposed System Implementation of
More informationPerceptron as a graph
Neural Networks Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University October 10 th, 2007 2005-2007 Carlos Guestrin 1 Perceptron as a graph 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0-6 -4-2
More informationArtificial Intelligence
Artificial Intelligence AI & Machine Learning & Neural Nets Marc Toussaint University of Stuttgart Winter 2018/19 Motivation: Neural networks became a central topic for Machine Learning and AI. But in
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 informationCrowd Scene Understanding with Coherent Recurrent Neural Networks
Crowd Scene Understanding with Coherent Recurrent Neural Networks Hang Su, Yinpeng Dong, Jun Zhu May 22, 2016 Hang Su, Yinpeng Dong, Jun Zhu IJCAI 2016 May 22, 2016 1 / 26 Outline 1 Introduction 2 LSTM
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 informationJOINT INTENT DETECTION AND SLOT FILLING USING CONVOLUTIONAL NEURAL NETWORKS. Puyang Xu, Ruhi Sarikaya. Microsoft Corporation
JOINT INTENT DETECTION AND SLOT FILLING USING CONVOLUTIONAL NEURAL NETWORKS Puyang Xu, Ruhi Sarikaya Microsoft Corporation ABSTRACT We describe a joint model for intent detection and slot filling based
More informationImage Captioning with Object Detection and Localization
Image Captioning with Object Detection and Localization Zhongliang Yang, Yu-Jin Zhang, Sadaqat ur Rehman, Yongfeng Huang, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
More informationThe exam is closed book, closed notes except your one-page (two-sided) cheat sheet.
CS 189 Spring 2015 Introduction to Machine Learning Final You have 2 hours 50 minutes for the exam. The exam is closed book, closed notes except your one-page (two-sided) cheat sheet. No calculators or
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 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 informationThe exam is closed book, closed notes except your one-page (two-sided) cheat sheet.
CS 189 Spring 2015 Introduction to Machine Learning Final You have 2 hours 50 minutes for the exam. The exam is closed book, closed notes except your one-page (two-sided) cheat sheet. No calculators or
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 informationCOMP9444 Neural Networks and Deep Learning 5. Geometry of Hidden Units
COMP9 8s Geometry of Hidden Units COMP9 Neural Networks and Deep Learning 5. Geometry of Hidden Units Outline Geometry of Hidden Unit Activations Limitations of -layer networks Alternative transfer functions
More informationINF 5860 Machine learning for image classification. Lecture 11: Visualization Anne Solberg April 4, 2018
INF 5860 Machine learning for image classification Lecture 11: Visualization Anne Solberg April 4, 2018 Reading material The lecture is based on papers: Deep Dream: https://research.googleblog.com/2015/06/inceptionism-goingdeeper-into-neural.html
More informationLecture 7: Neural network acoustic models in speech recognition
CS 224S / LINGUIST 285 Spoken Language Processing Andrew Maas Stanford University Spring 2017 Lecture 7: Neural network acoustic models in speech recognition Outline Hybrid acoustic modeling overview Basic
More informationBackpropagation and Neural Networks. Lecture 4-1
Lecture 4: Backpropagation and Neural Networks Lecture 4-1 Administrative Assignment 1 due Thursday April 20, 11:59pm on Canvas Lecture 4-2 Administrative Project: TA specialities and some project ideas
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 informationPair-wise Distance Metric Learning of Neural Network Model for Spoken Language Identification
INTERSPEECH 2016 September 8 12, 2016, San Francisco, USA Pair-wise Distance Metric Learning of Neural Network Model for Spoken Language Identification 2 1 Xugang Lu 1, Peng Shen 1, Yu Tsao 2, Hisashi
More informationConvolutional Networks for Text
CS11-747 Neural Networks for NLP Convolutional Networks for Text Graham Neubig Site https://phontron.com/class/nn4nlp2017/ An Example Prediction Problem: Sentence Classification I hate this movie very
More informationIntroduction to Neural Networks
Introduction to Neural Networks Machine Learning and Object Recognition 2016-2017 Course website: http://thoth.inrialpes.fr/~verbeek/mlor.16.17.php Biological motivation Neuron is basic computational unit
More informationDEEP LEARNING IN PYTHON. Introduction to deep learning
DEEP LEARNING IN PYTHON Introduction to deep learning Imagine you work for a bank You need to predict how many transactions each customer will make next year Example as seen by linear regression Age Bank
More informationDeep Learning Explained Module 6: Text classification with Recurrence (LSTM)
eep earning xplained Module 6: Text classification with Recurrence (STM) Sayan. Pathak, Ph.., Principal M Scientist, Microsoft Roland Fernandez, Senior Researcher, Microsoft Module outline Application:
More information1 Introduction. 3 Dataset and Features. 2 Prior Work. 3.1 Data Source. 3.2 Data Preprocessing
CS 229 Final Project Report Predicting the Likelihood of Response in a Messaging Application Tushar Paul (SUID: aritpaul), Kevin Shin (SUID: kevshin) December 16, 2016 1 Introduction A common feature of
More informationECE 6504: Deep Learning for Perception
ECE 6504: Deep Learning for Perception Topics: (Finish) Backprop Convolutional Neural Nets Dhruv Batra Virginia Tech Administrativia Presentation Assignments https://docs.google.com/spreadsheets/d/ 1m76E4mC0wfRjc4HRBWFdAlXKPIzlEwfw1-u7rBw9TJ8/
More informationDeep Learning. Yee Whye Teh (Oxford Statistics & DeepMind)
Deep Learning Yee Whye Teh (Oxford Statistics & DeepMind) http://csml.stats.ox.ac.uk/people/teh What is Machine Learning? Information Structure Prediction Decisions Actions data What is Machine Learning?
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 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 informationDeep 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,
More informationInstance-based Learning
Instance-based Learning Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University February 19 th, 2007 2005-2007 Carlos Guestrin 1 Why not just use Linear Regression? 2005-2007 Carlos Guestrin
More informationSEMANTIC COMPUTING. Lecture 9: Deep Learning: Recurrent Neural Networks (RNNs) TU Dresden, 21 December 2018
SEMANTIC COMPUTING Lecture 9: Deep Learning: Recurrent Neural Networks (RNNs) Dagmar Gromann International Center For Computational Logic TU Dresden, 21 December 2018 Overview Handling Overfitting Recurrent
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 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 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 informationNeural Networks. Single-layer neural network. CSE 446: Machine Learning Emily Fox University of Washington March 10, /10/2017
3/0/207 Neural Networks Emily Fox University of Washington March 0, 207 Slides adapted from Ali Farhadi (via Carlos Guestrin and Luke Zettlemoyer) Single-layer neural network 3/0/207 Perceptron as a neural
More informationDeep 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,
More informationCPSC 340: Machine Learning and Data Mining. Kernel Trick Fall 2017
CPSC 340: Machine Learning and Data Mining Kernel Trick Fall 2017 Admin Assignment 3: Due Friday. Midterm: Can view your exam during instructor office hours or after class this week. Digression: the other
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 informationDeep Learning in NLP. Horacio Rodríguez. AHLT Deep Learning 2 1
Deep Learning in NLP Horacio Rodríguez AHLT Deep Learning 2 1 Outline Introduction Short review of Distributional Semantics, Semantic spaces, VSM, Embeddings Embedding of words Embedding of more complex
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 informationIntroduction to Deep Learning for Facial Understanding Part III: Regional CNNs
Introduction to Deep Learning for Facial Understanding Part III: Regional CNNs Raymond Ptucha, Rochester Institute of Technology, USA Tutorial-9 May 19, 218 www.nvidia.com/dli R. Ptucha 18 1 Fair Use Agreement
More informationCSC321 Lecture 10: Automatic Differentiation
CSC321 Lecture 10: Automatic Differentiation Roger Grosse Roger Grosse CSC321 Lecture 10: Automatic Differentiation 1 / 23 Overview Implementing backprop by hand is like programming in assembly language.
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 179 Lecture 16. Logistic Regression & Parallel SGD
CS 179 Lecture 16 Logistic Regression & Parallel SGD 1 Outline logistic regression (stochastic) gradient descent parallelizing SGD for neural nets (with emphasis on Google s distributed neural net implementation)
More informationMathematics of Data. INFO-4604, Applied Machine Learning University of Colorado Boulder. September 5, 2017 Prof. Michael Paul
Mathematics of Data INFO-4604, Applied Machine Learning University of Colorado Boulder September 5, 2017 Prof. Michael Paul Goals In the intro lecture, every visualization was in 2D What happens when we
More informationTable of Contents. What Really is a Hidden Unit? Visualizing Feed-Forward NNs. Visualizing Convolutional NNs. Visualizing Recurrent NNs
Table of Contents What Really is a Hidden Unit? Visualizing Feed-Forward NNs Visualizing Convolutional NNs Visualizing Recurrent NNs Visualizing Attention Visualizing High Dimensional Data What do visualizations
More informationFace Recognition A Deep Learning Approach
Face Recognition A Deep Learning Approach Lihi Shiloh Tal Perl Deep Learning Seminar 2 Outline What about Cat recognition? Classical face recognition Modern face recognition DeepFace FaceNet Comparison
More informationSensor-based Semantic-level Human Activity Recognition using Temporal Classification
Sensor-based Semantic-level Human Activity Recognition using Temporal Classification Weixuan Gao gaow@stanford.edu Chuanwei Ruan chuanwei@stanford.edu Rui Xu ray1993@stanford.edu I. INTRODUCTION Human
More informationRecurrent Neural Networks
Recurrent Neural Networks Javier Béjar Deep Learning 2018/2019 Fall Master in Artificial Intelligence (FIB-UPC) Introduction Sequential data Many problems are described by sequences Time series Video/audio
More informationLecture note 7: Playing with convolutions in TensorFlow
Lecture note 7: Playing with convolutions in TensorFlow CS 20SI: TensorFlow for Deep Learning Research (cs20si.stanford.edu) Prepared by Chip Huyen ( huyenn@stanford.edu ) This lecture note is an unfinished
More informationEncoding RNNs, 48 End of sentence (EOS) token, 207 Exploding gradient, 131 Exponential function, 42 Exponential Linear Unit (ELU), 44
A Activation potential, 40 Annotated corpus add padding, 162 check versions, 158 create checkpoints, 164, 166 create input, 160 create train and validation datasets, 163 dropout, 163 DRUG-AE.rel file,
More informationPractical Deep Learning
Practical Deep Learning micha.codes / fastforwardlabs.com 1 / 70 deep learning can seem mysterious 2 / 70 let's nd a way to just build a function 3 / 70 Feed Forward Layer # X.shape == (512,) # output.shape
More informationCS6220: DATA MINING TECHNIQUES
CS6220: DATA MINING TECHNIQUES Image Data: Classification via Neural Networks Instructor: Yizhou Sun yzsun@ccs.neu.edu November 19, 2015 Methods to Learn Classification Clustering Frequent Pattern Mining
More informationMATLAB representation of neural network Outline Neural network with single-layer of neurons. Neural network with multiple-layer of neurons.
MATLAB representation of neural network Outline Neural network with single-layer of neurons. Neural network with multiple-layer of neurons. Introduction: Neural Network topologies (Typical Architectures)
More informationAttentionNet for Accurate Localization and Detection of Objects. (To appear in ICCV 2015)
AttentionNet for Accurate Localization and Detection of Objects. (To appear in ICCV 2015) Donggeun Yoo, Sunggyun Park, Joon-Young Lee, Anthony Paek, In So Kweon. State-of-the-art frameworks for object
More informationDeep Neural Networks:
Deep Neural Networks: Part II Convolutional Neural Network (CNN) Yuan-Kai Wang, 2016 Web site of this course: http://pattern-recognition.weebly.com source: CNN for ImageClassification, by S. Lazebnik,
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 informationDeep Learning on Graphs
Deep Learning on Graphs with Graph Convolutional Networks Hidden layer Hidden layer Input Output ReLU ReLU, 6 April 2017 joint work with Max Welling (University of Amsterdam) The success story of deep
More informationThis Talk. 1) Node embeddings. Map nodes to low-dimensional embeddings. 2) Graph neural networks. Deep learning architectures for graphstructured
Representation Learning on Networks, snap.stanford.edu/proj/embeddings-www, WWW 2018 1 This Talk 1) Node embeddings Map nodes to low-dimensional embeddings. 2) Graph neural networks Deep learning architectures
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 informationBack Propagation and Other Differentiation Algorithms. Sargur N. Srihari
Back Propagation and Other Differentiation Algorithms Sargur N. srihari@cedar.buffalo.edu 1 Topics (Deep Feedforward Networks) Overview 1. Example: Learning XOR 2. Gradient-Based Learning 3. Hidden Units
More informationCS 224d: Assignment #1
Due date: assignment) 4/19 11:59 PM PST (You are allowed to use three (3) late days maximum for this These questions require thought, but do not require long answers. Please be as concise as possible.
More informationEE 511 Neural Networks
Slides adapted from Ali Farhadi, Mari Ostendorf, Pedro Domingos, Carlos Guestrin, and Luke Zettelmoyer, Andrei Karpathy EE 511 Neural Networks Instructor: Hanna Hajishirzi hannaneh@washington.edu Computational
More information