Introduction to Deep Learning. Welcome. deeplearning.ai. Andrew Ng

Size: px
Start display at page:

Download "Introduction to Deep Learning. Welcome. deeplearning.ai. Andrew Ng"

Transcription

1 Introduction to Deep Learning Welcome deeplearning.ai

2 AI is the new Electricity Electricity had once transformed countless industries: transportation, manufacturing, healthcare, communications, and more AI will now bring about an equally big transformation.

3 What you ll learn Courses in this sequence (Specialization): 1. Neural Networks and Deep Learning 2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 3. Structuring your Machine Learning project 4. Convolutional Neural Networks 5. Natural Language Processing: Building sequence models

4 Introduction to Deep Learning What is a deeplearning.ai Neural Network?

5 Housing Price Prediction price size of house

6 Housing Price Prediction

7 Housing Price Prediction size! " #bedrooms zip code! #! $ y wealth! %

8 Introduction to Deep Learning Supervised Learning deeplearning.ai with Neural Networks

9 Supervised Learning Input(x) Home features Ad, user info Image Audio English Image, Radar info Output (y) Price Click on ad? (0/1) Object (1,,1000) Text transcript Chinese Position of other cars Application Real Estate Online Advertising Photo tagging Speech recognition Machine translation Autonomous driving

10 Neural Network examples Standard NN Convolutional NN Recurrent NN

11 Supervised Learning Structured Data Unstructured Data Size #bedrooms User Age Ad Id Price (1000$s) Click Image Audio Four scores and seven years ago Text

12 Introduction to Neural Networks Why is Deep deeplearning.ai Learning taking off?

13 Scale drives deep learning progress Performance Amount of data

14 Scale drives deep learning progress Data Idea Computation Algorithms Experiment Code

15 Introduction to Neural Networks About this Course deeplearning.ai

16 Courses in this Specialization 1. Neural Networks and Deep Learning 2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 3. Structuring your Machine Learning project 4. Convolutional Neural Networks 5. Natural Language Processing: Building sequence models

17 Outline of this Course Week 1: Introduction Week 2: Basics of Neural Network programming Week 3: One hidden layer Neural Networks Week 4: Deep Neural Networks

18 Introduction to Deep Learning Supervised Learning deeplearning.ai with Neural Networks

19 Supervised Learning Input(x) Home features Ad, user info Image Audio English Image, Radar info Output (y) Price Click on ad? (0/1) Object (1,,1000) Text transcript Chinese Position of other cars Application Real Estate Online Advertising Photo tagging Speech recognition Machine translation Autonomous driving

20 Neural Network examples Standard NN Convolutional NN Recurrent NN

21 Supervised Learning Structured Data Unstructured Data Size #bedrooms User Age Ad Id Price (1000$s) Click Image Audio Four scores and seven years ago Text

22 Introduction to Neural Networks Why is Deep deeplearning.ai Learning taking off?

23 Scale drives deep learning progress Performance Amount of data

24 Scale drives deep learning progress Data Idea Computation Algorithms Experiment Code

25 Introduction to Neural Networks About this Course deeplearning.ai

26 Courses in this Specialization 1. Neural Networks and Deep Learning 2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 3. Structuring your Machine Learning project 4. Convolutional Neural Networks 5. Natural Language Processing: Building sequence models

27 Outline of this Course Week 1: Introduction Week 2: Basics of Neural Network programming Week 3: One hidden layer Neural Networks Week 4: Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering

More information

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach #BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying

More information

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology

More information

Frequently Asked Questions about Music Education at IU

Frequently Asked Questions about Music Education at IU How do I obtain an ID card? 501 N. Union is the location of Campus Card Services (855-8711) in the Ashton complex. The picture for your photo ID will be taken then, and you can also get a campus bus pass

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Model Ensemble for Click Prediction in Bing Search Ads

Model Ensemble for Click Prediction in Bing Search Ads Model Ensemble for Click Prediction in Bing Search Ads Xiaoliang Ling Microsoft Bing xiaoling@microsoft.com Hucheng Zhou Microsoft Research huzho@microsoft.com Weiwei Deng Microsoft Bing dedeng@microsoft.com

More information

Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks

Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim Samsung Electronics Co., Ltd. Seoul, South Korea {dev.chaplot,eunhee.rhim,jihie.kim}@samsung.com

More information

Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski

Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Problem Statement and Background Given a collection of 8th grade science questions, possible answer

More information

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

POS tagging of Chinese Buddhist texts using Recurrent Neural Networks

POS tagging of Chinese Buddhist texts using Recurrent Neural Networks POS tagging of Chinese Buddhist texts using Recurrent Neural Networks Longlu Qin Department of East Asian Languages and Cultures longlu@stanford.edu Abstract Chinese POS tagging, as one of the most important

More information

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.

More information

Introduction on Lean, six sigma and Lean game. Remco Paulussen, Statistics Netherlands Anne S. Trolie, Statistics Norway

Introduction on Lean, six sigma and Lean game. Remco Paulussen, Statistics Netherlands Anne S. Trolie, Statistics Norway Introduction on Lean, six sigma and Lean game Remco Paulussen, Statistics Netherlands Anne S. Trolie, Statistics Norway 1 Lean is. a philosophy a method a set of tools Waste reduction User value Create

More information

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,

More information

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate

More information

Calibration of Confidence Measures in Speech Recognition

Calibration of Confidence Measures in Speech Recognition Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE

More information

How to set up gradebook categories in Moodle 2.

How to set up gradebook categories in Moodle 2. How to set up gradebook categories in Moodle 2. It is possible to set up the gradebook to show divisions in time such as semesters and quarters by using categories. For example, Semester 1 = main category

More information

Human Emotion Recognition From Speech

Human Emotion Recognition From Speech RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati

More information

Knowledge Transfer in Deep Convolutional Neural Nets

Knowledge Transfer in Deep Convolutional Neural Nets Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract

More information

CSL465/603 - Machine Learning

CSL465/603 - Machine Learning CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am

More information

Functional Nutrition Application

Functional Nutrition Application 1 Functional Nutrition Application For Fall 2017 Program Description The Functional Nutrition program at PCC Institute for Health Professionals will provide you a solid foundation to start your professional

More information

Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках

Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках Тарасов Д. С. (dtarasov3@gmail.com) Интернет-портал reviewdot.ru, Казань,

More information

IAT 888: Metacreation Machines endowed with creative behavior. Philippe Pasquier Office 565 (floor 14)

IAT 888: Metacreation Machines endowed with creative behavior. Philippe Pasquier Office 565 (floor 14) IAT 888: Metacreation Machines endowed with creative behavior Philippe Pasquier Office 565 (floor 14) pasquier@sfu.ca Outline of today's lecture A little bit about me A little bit about you What will that

More information

Generative models and adversarial training

Generative models and adversarial training Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?

More information

Getting Started Guide

Getting Started Guide Getting Started Guide Getting Started with Voki Classroom Oddcast, Inc. Published: July 2011 Contents: I. Registering for Voki Classroom II. Upgrading to Voki Classroom III. Getting Started with Voki Classroom

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

Lecture 1: Basic Concepts of Machine Learning

Lecture 1: Basic Concepts of Machine Learning Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010

More information

BANGLA TO ENGLISH TEXT CONVERSION USING OPENNLP TOOLS

BANGLA TO ENGLISH TEXT CONVERSION USING OPENNLP TOOLS Daffodil International University Institutional Repository DIU Journal of Science and Technology Volume 8, Issue 1, January 2013 2013-01 BANGLA TO ENGLISH TEXT CONVERSION USING OPENNLP TOOLS Uddin, Sk.

More information

arxiv: v1 [cs.lg] 7 Apr 2015

arxiv: v1 [cs.lg] 7 Apr 2015 Transferring Knowledge from a RNN to a DNN William Chan 1, Nan Rosemary Ke 1, Ian Lane 1,2 Carnegie Mellon University 1 Electrical and Computer Engineering, 2 Language Technologies Institute Equal contribution

More information

Get a Smart Start with Youth

Get a Smart Start with Youth Toolkit work bene ts youth Get a Smart Start with Youth Y O U T H I N T R A N S I T I O N Toolkit Overview Using the Toolkit TOOLKIT OVERVIEW The core component of the Get a Smart Start & Take Charge Toolkit

More information

Exploration. CS : Deep Reinforcement Learning Sergey Levine

Exploration. CS : Deep Reinforcement Learning Sergey Levine Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?

More information

Master s Programme in Computer, Communication and Information Sciences, Study guide , ELEC Majors

Master s Programme in Computer, Communication and Information Sciences, Study guide , ELEC Majors Master s Programme in Computer, Communication and Information Sciences, Study guide 2015-2016, ELEC Majors Sisällysluettelo PS=pääsivu, AS=alasivu PS: 1 Acoustics and Audio Technology... 4 Objectives...

More information

FIS Learning Management System Activities

FIS Learning Management System Activities FIS Learning Management System Activities This document outlines: Course Catalog View Event Information o General o Objectives o Agenda o Prerequisites o Schedule o Catalog Training Calendars Enrollment

More information

A study of speaker adaptation for DNN-based speech synthesis

A study of speaker adaptation for DNN-based speech synthesis A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,

More information

Soft Computing based Learning for Cognitive Radio

Soft Computing based Learning for Cognitive Radio Int. J. on Recent Trends in Engineering and Technology, Vol. 10, No. 1, Jan 2014 Soft Computing based Learning for Cognitive Radio Ms.Mithra Venkatesan 1, Dr.A.V.Kulkarni 2 1 Research Scholar, JSPM s RSCOE,Pune,India

More information

Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

More information

A Review: Speech Recognition with Deep Learning Methods

A Review: Speech Recognition with Deep Learning Methods Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 5, May 2015, pg.1017

More information

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION Mitchell McLaren 1, Yun Lei 1, Luciana Ferrer 2 1 Speech Technology and Research Laboratory, SRI International, California, USA 2 Departamento

More information

2/15/13. POS Tagging Problem. Part-of-Speech Tagging. Example English Part-of-Speech Tagsets. More Details of the Problem. Typical Problem Cases

2/15/13. POS Tagging Problem. Part-of-Speech Tagging. Example English Part-of-Speech Tagsets. More Details of the Problem. Typical Problem Cases POS Tagging Problem Part-of-Speech Tagging L545 Spring 203 Given a sentence W Wn and a tagset of lexical categories, find the most likely tag T..Tn for each word in the sentence Example Secretariat/P is/vbz

More information

Axiom 2013 Team Description Paper

Axiom 2013 Team Description Paper Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association

More information

arxiv: v1 [cs.lg] 15 Jun 2015

arxiv: v1 [cs.lg] 15 Jun 2015 Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy arxiv:1506.04477v1 [cs.lg] 15 Jun 2015 Sang-Woo Lee Min-Oh Heo School of Computer Science and

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Artificial Neural Networks

Artificial Neural Networks Artificial Neural Networks Andres Chavez Math 382/L T/Th 2:00-3:40 April 13, 2010 Chavez2 Abstract The main interest of this paper is Artificial Neural Networks (ANNs). A brief history of the development

More information

On the Formation of Phoneme Categories in DNN Acoustic Models

On the Formation of Phoneme Categories in DNN Acoustic Models On the Formation of Phoneme Categories in DNN Acoustic Models Tasha Nagamine Department of Electrical Engineering, Columbia University T. Nagamine Motivation Large performance gap between humans and state-

More information

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers

More information

Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors

Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-6) Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors Sang-Woo Lee,

More information

भ रत य व ज ञ न व क ष ए अन स ध न स स थ न वतर पवत

भ रत य व ज ञ न व क ष ए अन स ध न स स थ न वतर पवत ADVT. NO.: 01/2017 (Apply on or before February 15, 2017) Indian Institute of Science Education and Research, Tirupati, is a premier autonomous Institution established by the Ministry of Human Resource

More information

Digital Path. Here is a look at the organization and features of the program. After logging in, click Pearson Content on the Programs channel.

Digital Path. Here is a look at the organization and features of the program. After logging in, click Pearson Content on the Programs channel. Digital Path Introduction Content Organization This guide explores the digital content on myworldhistory.com and look at how it supports students to connect, experience, and understand their world. All

More information

Cultivating DNN Diversity for Large Scale Video Labelling

Cultivating DNN Diversity for Large Scale Video Labelling Cultivating DNN Diversity for Large Scale Video Labelling Mikel Bober-Irizar mikel@mxbi.net Sameed Husain sameed.husain@surrey.ac.uk Miroslaw Bober m.bober@surrey.ac.uk Eng-Jon Ong e.ong@surrey.ac.uk Abstract

More information

Rule-based Expert Systems

Rule-based Expert Systems Rule-based Expert Systems What is knowledge? is a theoretical or practical understanding of a subject or a domain. is also the sim of what is currently known, and apparently knowledge is power. Those who

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Welcome to Georgia Tech!

Welcome to Georgia Tech! SelfGuided Tour Welcome to Georgia Tech! We are so glad you have decided to conduct a self-guided tour of campus! During your visit, you will notice that we offer a traditional green campus within an urban

More information

You Gotta Go Somewhere Prep for College Calendar

You Gotta Go Somewhere Prep for College Calendar You Gotta Go Somewhere Prep for College Calendar F R E S H M A N Y E A R It is never too early to plan for the future! Enroll in a college preparatory curriculum Study hard and do your best in school From

More information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

Boston MA Visitors Street Map By American Map

Boston MA Visitors Street Map By American Map Boston MA Visitors Street Map By American Map If you are searching for a book Boston MA Visitors Street Map by American Map in pdf form, then you've come to faithful site. We presented the utter option

More information

Bachelor of Engineering

Bachelor of Engineering Bachelor of Engineering Technology KEY INFORMATION FOR STUDENTS Bachelor of Engineering Technology ENTRY REQUIREMENTS Location Duration Delivery Credits Level Start Dunedin Three years full-time; part-time

More information

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders

More information

Forget catastrophic forgetting: AI that learns after deployment

Forget catastrophic forgetting: AI that learns after deployment Forget catastrophic forgetting: AI that learns after deployment Anatoly Gorshechnikov CTO, Neurala 1 Neurala at a glance Programming neural networks on GPUs since circa 2 B.C. Founded in 2006 expecting

More information

Speaker Identification by Comparison of Smart Methods. Abstract

Speaker Identification by Comparison of Smart Methods. Abstract Journal of mathematics and computer science 10 (2014), 61-71 Speaker Identification by Comparison of Smart Methods Ali Mahdavi Meimand Amin Asadi Majid Mohamadi Department of Electrical Department of Computer

More information

MEDICAL ACUPUNCTURE FOR VETERINARIANS

MEDICAL ACUPUNCTURE FOR VETERINARIANS MEDICAL ACUPUNCTURE FOR VETERINARIANS Center for Comparative and Integrative Pain Medicine Merging Modern Medicine with Ancient Wisdom Course Information Why Medical Acupuncture for Veterinarians? Medical

More information

Navigating the PhD Options in CMS

Navigating the PhD Options in CMS Navigating the PhD Options in CMS This document gives an overview of the typical student path through the four Ph.D. programs in the CMS department ACM, CDS, CS, and CMS. Note that it is not a replacement

More information

Learning to Schedule Straight-Line Code

Learning to Schedule Straight-Line Code Learning to Schedule Straight-Line Code Eliot Moss, Paul Utgoff, John Cavazos Doina Precup, Darko Stefanović Dept. of Comp. Sci., Univ. of Mass. Amherst, MA 01003 Carla Brodley, David Scheeff Sch. of Elec.

More information

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration INTERSPEECH 2013 Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration Yan Huang, Dong Yu, Yifan Gong, and Chaojun Liu Microsoft Corporation, One

More information

OFFICIAL DOCUMENT. Foreign Credits, Inc. Jawaharlal Nehru Technological University

OFFICIAL DOCUMENT. Foreign Credits, Inc.  Jawaharlal Nehru Technological University (^ForeignCredits (224)521-0170 : info@forelgncredlts.cdm Evaluation ID: 1234S6-849491-7JK9031 U.S. Equivalency: U.S. Credits: U.S. GPA: Bachelor of Science degree In Electronics and Communication Engineering

More information

2017 Florence, Italty Conference Abstract

2017 Florence, Italty Conference Abstract 2017 Florence, Italty Conference Abstract Florence, Italy October 23-25, 2017 Venue: NILHOTEL ADD: via Eugenio Barsanti 27 a/b - 50127 Florence, Italy PHONE: (+39) 055 795540 FAX: (+39) 055 79554801 EMAIL:

More information

Dialog-based Language Learning

Dialog-based Language Learning Dialog-based Language Learning Jason Weston Facebook AI Research, New York. jase@fb.com arxiv:1604.06045v4 [cs.cl] 20 May 2016 Abstract A long-term goal of machine learning research is to build an intelligent

More information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

Natural Language Processing. George Konidaris

Natural Language Processing. George Konidaris Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans

More information

North Carolina Community Colleges Golden LEAF Scholars Program Two-Year Colleges Student Application

North Carolina Community Colleges Golden LEAF Scholars Program Two-Year Colleges Student Application North Carolina Community Colleges Golden LEAF Scholars Program Two-Year Colleges Student Application Instructions: Complete this application and return the completed application to the college s Financial

More information

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,

More information

Computers Change the World

Computers Change the World Computers Change the World Computing is Changing the World Activity 1.1.1 Computing Is Changing the World Students pick a grand challenge and consider how mobile computing, the Internet, Big Data, and

More information

LEARN. LEAD. DISCOVER.

LEARN. LEAD. DISCOVER. LEARN. LEAD. DISCOVER. WHAT IS MMI? Your Master s Degree in 12 months The MMI Program is an accelerated professional degree at the University of Toronto Mississauga (UTM) which focuses on the management

More information

An Introduction to Simio for Beginners

An Introduction to Simio for Beginners An Introduction to Simio for Beginners C. Dennis Pegden, Ph.D. This white paper is intended to introduce Simio to a user new to simulation. It is intended for the manufacturing engineer, hospital quality

More information

Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma

Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma Adam Abdulhamid Stanford University 450 Serra Mall, Stanford, CA 94305 adama94@cs.stanford.edu Abstract With the introduction

More information

Home Access Center. Connecting Parents to Fulton County Schools

Home Access Center. Connecting Parents to Fulton County Schools Home Access Center Connecting Parents to Fulton County Schools What is Home Access Center? Website available to parents (and at site discretion, students) that is a real-time look at student data The data

More information

Learning Methods in Multilingual Speech Recognition

Learning Methods in Multilingual Speech Recognition Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex

More information

TRANSFER LEARNING OF WEAKLY LABELLED AUDIO. Aleksandr Diment, Tuomas Virtanen

TRANSFER LEARNING OF WEAKLY LABELLED AUDIO. Aleksandr Diment, Tuomas Virtanen TRANSFER LEARNING OF WEAKLY LABELLED AUDIO Aleksandr Diment, Tuomas Virtanen Tampere University of Technology Laboratory of Signal Processing Korkeakoulunkatu 1, 33720, Tampere, Finland firstname.lastname@tut.fi

More information

Purchase College STATE UNIVERSITY OF NEW YORK

Purchase College STATE UNIVERSITY OF NEW YORK IMPORTANT: Your application was selected for review in a process called "Verification". We will be comparing information from your FAFSA with the documentation we have requested. If there are differences,

More information

DIRECT ADAPTATION OF HYBRID DNN/HMM MODEL FOR FAST SPEAKER ADAPTATION IN LVCSR BASED ON SPEAKER CODE

DIRECT ADAPTATION OF HYBRID DNN/HMM MODEL FOR FAST SPEAKER ADAPTATION IN LVCSR BASED ON SPEAKER CODE 2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) DIRECT ADAPTATION OF HYBRID DNN/HMM MODEL FOR FAST SPEAKER ADAPTATION IN LVCSR BASED ON SPEAKER CODE Shaofei Xue 1

More information

Deep Neural Network Language Models

Deep Neural Network Language Models Deep Neural Network Language Models Ebru Arısoy, Tara N. Sainath, Brian Kingsbury, Bhuvana Ramabhadran IBM T.J. Watson Research Center Yorktown Heights, NY, 10598, USA {earisoy, tsainath, bedk, bhuvana}@us.ibm.com

More information

Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

More information

Spring North Carolina Community Colleges Golden LEAF Scholars Program Two-Year Colleges

Spring North Carolina Community Colleges Golden LEAF Scholars Program Two-Year Colleges Spring 2017 North Carolina Community Colleges Instructions: Complete the following application and return to the college s Financial Aid Office. Application Deadline: March 1, 2017 March 7, 2017 Contact:

More information

A deep architecture for non-projective dependency parsing

A deep architecture for non-projective dependency parsing Universidade de São Paulo Biblioteca Digital da Produção Intelectual - BDPI Departamento de Ciências de Computação - ICMC/SCC Comunicações em Eventos - ICMC/SCC 2015-06 A deep architecture for non-projective

More information

Journal title ISSN Full text from

Journal title ISSN Full text from Title listings ejournals Management ejournals Database and Specialist ejournals Collections Emerald Insight Management ejournals Database Journal title ISSN Full text from Accounting, Finance & Economics

More information

JOB OUTLOOK 2018 NOVEMBER 2017 FREE TO NACE MEMBERS $52.00 NONMEMBER PRICE NATIONAL ASSOCIATION OF COLLEGES AND EMPLOYERS

JOB OUTLOOK 2018 NOVEMBER 2017 FREE TO NACE MEMBERS $52.00 NONMEMBER PRICE NATIONAL ASSOCIATION OF COLLEGES AND EMPLOYERS NOVEMBER 2017 FREE TO NACE MEMBERS $52.00 NONMEMBER PRICE JOB OUTLOOK 2018 NATIONAL ASSOCIATION OF COLLEGES AND EMPLOYERS 62 Highland Avenue, Bethlehem, PA 18017 www.naceweb.org 610,868.1421 TABLE OF CONTENTS

More information

Driving Author Engagement through IEEE Collabratec

Driving Author Engagement through IEEE Collabratec Driving Author Engagement through IEEE Collabratec Gianluca Setti 2013-2014 IEEE Vice President for Publication Services and Products Professor of Engineering, University of Ferrara gianluca.setti@unife.it

More information

Part I. Figuring out how English works

Part I. Figuring out how English works 9 Part I Figuring out how English works 10 Chapter One Interaction and grammar Grammar focus. Tag questions Introduction. How closely do you pay attention to how English is used around you? For example,

More information

Pharmacy Technician Program

Pharmacy Technician Program Pharmacy Technician Program 12800 Abrams Road Dallas, Texas 75243-2199 972.238.6950 www.richlandcollege.edu/hp Health Professions Division Pharmacy Technician Program Application Packet Equal Opportunity

More information

Device Independence and Extensibility in Gesture Recognition

Device Independence and Extensibility in Gesture Recognition Device Independence and Extensibility in Gesture Recognition Jacob Eisenstein, Shahram Ghandeharizadeh, Leana Golubchik, Cyrus Shahabi, Donghui Yan, Roger Zimmermann Department of Computer Science University

More information

Continuing Education Unit Program Course Catalog

Continuing Education Unit Program Course Catalog Continuing Education Unit Program 2016 Course Catalog Continuing Education Unit (CEU) Course Catalog TABLE OF CONTENTS Overview 3 CEU Program 4 Design 5 Alexander Girard 6 A Night with Nelson 6 Eames Design:

More information

A Deep Bag-of-Features Model for Music Auto-Tagging

A Deep Bag-of-Features Model for Music Auto-Tagging 1 A Deep Bag-of-Features Model for Music Auto-Tagging Juhan Nam, Member, IEEE, Jorge Herrera, and Kyogu Lee, Senior Member, IEEE latter is often referred to as music annotation and retrieval, or simply

More information

Data Fusion Through Statistical Matching

Data Fusion Through Statistical Matching A research and education initiative at the MIT Sloan School of Management Data Fusion Through Statistical Matching Paper 185 Peter Van Der Puttan Joost N. Kok Amar Gupta January 2002 For more information,

More information

Interaction Design Considerations for an Aircraft Carrier Deck Agent-based Simulation

Interaction Design Considerations for an Aircraft Carrier Deck Agent-based Simulation Interaction Design Considerations for an Aircraft Carrier Deck Agent-based Simulation Miles Aubert (919) 619-5078 Miles.Aubert@duke. edu Weston Ross (505) 385-5867 Weston.Ross@duke. edu Steven Mazzari

More information

Deep Facial Action Unit Recognition from Partially Labeled Data

Deep Facial Action Unit Recognition from Partially Labeled Data Deep Facial Action Unit Recognition from Partially Labeled Data Shan Wu 1, Shangfei Wang,1, Bowen Pan 1, and Qiang Ji 2 1 University of Science and Technology of China, Hefei, Anhui, China 2 Rensselaer

More information

arxiv: v2 [cs.ir] 22 Aug 2016

arxiv: v2 [cs.ir] 22 Aug 2016 Exploring Deep Space: Learning Personalized Ranking in a Semantic Space arxiv:1608.00276v2 [cs.ir] 22 Aug 2016 ABSTRACT Jeroen B. P. Vuurens The Hague University of Applied Science Delft University of

More information

ONLINE COURSES. Flexibility to Meet Middle and High School Students at Their Point of Need

ONLINE COURSES. Flexibility to Meet Middle and High School Students at Their Point of Need ONLINE COURSES Flexibility to Meet Middle and High School Students at Their Point of Need 88 FuelEd Online Courses Standards-based online courses for middle and high school Struggling Seeking Greater Academic

More information

Full-time MBA Program Distinguish Yourself.

Full-time MBA Program Distinguish Yourself. Full-time MBA Program Distinguish Yourself. uconnmba@business.uconn.edu +1 (860) 728-2440 mba.uconn.edu Greetings! Thank you for your interest in our graduate business programs at the University of Connecticut

More information

The University of Akron. College Credit Plus Program

The University of Akron. College Credit Plus Program The University of Akron College Credit Plus Program What is the College Credit Plus Program? The College Credit Plus Program was created by the Ohio Legislature to allow students to earn college credits

More information