AI Foundations using TensorFlow

Size: px
Start display at page:

Download "AI Foundations using TensorFlow"

Transcription

1 GreyCampus AI Foundations using TensorFlow 32 hours 8 weeks 2 hours/day

2 Program Overview Artificial Intelligence (AI) has been transforming multiple industries. At the same time, all the major advancements in AI can be attributed to the field of Deep Learning. If you are looking to break into AI, this program will help you become good at Deep Learning, one of the most sought after skill in technology. This program provides a thorough introduction to Deep Learning and how it can be applied to various Natural Language Processing (NLP) and Computer Vision tasks. You will not only learn the theoretical foundations but also gain practice in implementing these concepts using TensorFlow, an Industry leading framework to build Deep Learning models. Throughout this program, students will learn practical know-how to effectively apply deep Learning techniques to work on new problem. The program will also draw from numerous case studies and applications, so that you ll also learn how to apply learning algorithms to build Chat bots, computer vision etc.

3 Program Benefits Live Sessions Live and interactive instructor sessions Top Instructors Learn from the practising experts in the industry Hands-on Labs Sessions include hands-on projects in live labs LMS Learning Managements System to reinforce learningt Case Studies Real-life cases from industry part of the curriculum Projects Experience end-to-end project completion in the program

4 Program Outcome At the end of this program the participant would: Understand Core Machine Learning and Deep Learning concepts Learn to optimize Deep Learning models. Understand Key Deep Learning algorithms e.g. CNN, RNN, LSTM, GRU Learn to utilize TensorFlow, a Deep Learning framework, to build DL models Apply Deep Learning models on various NLP(Natural Language Processing) and Computer Vision tasks. Learn to deploy a trained model in Production

5 Our Faculty Kumar Rahul Data Analytics - IIMB Rahul has over 12 years experience in the data analytics space working with top companies like Deloitte where he was involved in software development, business consulting, analytical modeling and leading process improvement initiatives. Rahul teaches at IIM Bangalore and an active thought leader in the data science space. Rahul has led several global projects involving complex business analytics with Fortune 50 companies. Lavita Singhania Data Scientist Lavita has over 9 years experience as a data scientist and business analyst. She is currently a Senior Consultant at Cartesian Consulting where she has worked on implementing the entire Customer Lifecycle management journey using advanced Analytics and contributed to significantly increasing margins of top brands. She has facilitated around 450+ hours of training in the data science field.

6 Program Curriculum Understanding AI Using TensorFlow Understanding Deep Learning Improving Deep Learning Networks: Hyperparameter tuning, Optimization Convolution Neural Networks CNN Architectures and Transfer Learning Representing Textual data using Word2Vec models Recurrent Neural Networks Seq2Seq models and Machine Translation Deploying a trained model Building a Chatbot * Participants will undertake any two projects with an end-to-end development experience

7 ABOUT GREYCAMPUS GreyCampus transforms careers through skills and certification training. We are a leading provider of training for working professionals in the areas of data science, artificial intelligence, project management, quality management, cyber security and more. Our suite of programs are constantly upgraded with the latest career enabling skills required by the industry. More than 100,000 professionals globally have taken advantage of our courses to address their career goals. TRAINED OVER 100,000 PROFESSIONALS REACH ACROSS 50+ COUNTRIES 500+ EXPERT FACULTY FROM INDUSTRY Become certified professional For more information, please contact us at (91)

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

Top US Tech Talent for the Top China Tech Company

Top US Tech Talent for the Top China Tech Company THE FALL 2017 US RECRUITING TOUR Top US Tech Talent for the Top China Tech Company INTERVIEWS IN 7 CITIES Tour Schedule CITY Boston, MA New York, NY Pittsburgh, PA Urbana-Champaign, IL Ann Arbor, MI Los

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

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

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

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

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

Citrine Informatics. The Latest from Citrine. Citrine Informatics. The data analytics platform for the physical world

Citrine Informatics. The Latest from Citrine. Citrine Informatics. The data analytics platform for the physical world Citrine Informatics The data analytics platform for the physical world The Latest from Citrine Summit on Data and Analytics for Materials Research 31 October 2016 Our Mission is Simple Add as much value

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

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

arxiv: v4 [cs.cl] 28 Mar 2016

arxiv: v4 [cs.cl] 28 Mar 2016 LSTM-BASED DEEP LEARNING MODELS FOR NON- FACTOID ANSWER SELECTION Ming Tan, Cicero dos Santos, Bing Xiang & Bowen Zhou IBM Watson Core Technologies Yorktown Heights, NY, USA {mingtan,cicerons,bingxia,zhou}@us.ibm.com

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

Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study

Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information

More information

MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE

MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Master of Science (M.S.) Major in Computer Science 1 MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Major Program The programs in computer science are designed to prepare students for doctoral research,

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

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

ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology

ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology Tiancheng Zhao CMU-LTI-16-006 Language Technologies Institute School of Computer Science Carnegie Mellon

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

Reinforcement Learning by Comparing Immediate Reward

Reinforcement Learning by Comparing Immediate Reward Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate

More information

THE world surrounding us involves multiple modalities

THE world surrounding us involves multiple modalities 1 Multimodal Machine Learning: A Survey and Taxonomy Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency arxiv:1705.09406v2 [cs.lg] 1 Aug 2017 Abstract Our experience of the world is multimodal

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

Second Exam: Natural Language Parsing with Neural Networks

Second Exam: Natural Language Parsing with Neural Networks Second Exam: Natural Language Parsing with Neural Networks James Cross May 21, 2015 Abstract With the advent of deep learning, there has been a recent resurgence of interest in the use of artificial neural

More information

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1 Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial

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

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

Massachusetts Institute of Technology Tel: Massachusetts Avenue Room 32-D558 MA 02139

Massachusetts Institute of Technology Tel: Massachusetts Avenue  Room 32-D558 MA 02139 Hariharan Narayanan Massachusetts Institute of Technology Tel: 773.428.3115 LIDS har@mit.edu 77 Massachusetts Avenue http://www.mit.edu/~har Room 32-D558 MA 02139 EMPLOYMENT Massachusetts Institute of

More information

HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION

HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION Atul Laxman Katole 1, Krishna Prasad Yellapragada 1, Amish Kumar Bedi 1, Sehaj Singh Kalra 1 and Mynepalli Siva Chaitanya 1 1 Samsung

More information

Knowledge based expert systems D H A N A N J A Y K A L B A N D E

Knowledge based expert systems D H A N A N J A Y K A L B A N D E Knowledge based expert systems D H A N A N J A Y K A L B A N D E What is a knowledge based system? A Knowledge Based System or a KBS is a computer program that uses artificial intelligence to solve problems

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

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention Damien Teney 1, Peter Anderson 2*, David Golub 4*, Po-Sen Huang 3, Lei Zhang 3, Xiaodong He 3, Anton van den Hengel 1 1

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

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing Ask Me Anything: Dynamic Memory Networks for Natural Language Processing Ankit Kumar*, Ozan Irsoy*, Peter Ondruska*, Mohit Iyyer*, James Bradbury, Ishaan Gulrajani*, Victor Zhong*, Romain Paulus, Richard

More information

arxiv: v1 [cs.cl] 27 Apr 2016

arxiv: v1 [cs.cl] 27 Apr 2016 The IBM 2016 English Conversational Telephone Speech Recognition System George Saon, Tom Sercu, Steven Rennie and Hong-Kwang J. Kuo IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598 gsaon@us.ibm.com

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

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

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

CS224d Deep Learning for Natural Language Processing. Richard Socher, PhD

CS224d Deep Learning for Natural Language Processing. Richard Socher, PhD CS224d Deep Learning for Natural Language Processing, PhD Welcome 1. CS224d logis7cs 2. Introduc7on to NLP, deep learning and their intersec7on 2 Course Logis>cs Instructor: (Stanford PhD, 2014; now Founder/CEO

More information

A Reinforcement Learning Variant for Control Scheduling

A Reinforcement Learning Variant for Control Scheduling A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement

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

Residual Stacking of RNNs for Neural Machine Translation

Residual Stacking of RNNs for Neural Machine Translation Residual Stacking of RNNs for Neural Machine Translation Raphael Shu The University of Tokyo shu@nlab.ci.i.u-tokyo.ac.jp Akiva Miura Nara Institute of Science and Technology miura.akiba.lr9@is.naist.jp

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

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

A Pipelined Approach for Iterative Software Process Model

A Pipelined Approach for Iterative Software Process Model A Pipelined Approach for Iterative Software Process Model Ms.Prasanthi E R, Ms.Aparna Rathi, Ms.Vardhani J P, Mr.Vivek Krishna Electronics and Radar Development Establishment C V Raman Nagar, Bangalore-560093,

More information

Knowledge-Based - Systems

Knowledge-Based - Systems Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University

More information

What Can Neural Networks Teach us about Language? Graham Neubig a2-dlearn 11/18/2017

What Can Neural Networks Teach us about Language? Graham Neubig a2-dlearn 11/18/2017 What Can Neural Networks Teach us about Language? Graham Neubig a2-dlearn 11/18/2017 Supervised Training of Neural Networks for Language Training Data Training Model this is an example the cat went to

More information

Information System Design and Development (Advanced Higher) Unit. level 7 (12 SCQF credit points)

Information System Design and Development (Advanced Higher) Unit. level 7 (12 SCQF credit points) Information System Design and Development (Advanced Higher) Unit SCQF: level 7 (12 SCQF credit points) Unit code: H226 77 Unit outline The general aim of this Unit is for learners to develop a deep knowledge

More information

Henry Tirri* Petri Myllymgki

Henry Tirri* Petri Myllymgki From: AAAI Technical Report SS-93-04. Compilation copyright 1993, AAAI (www.aaai.org). All rights reserved. Bayesian Case-Based Reasoning with Neural Networks Petri Myllymgki Henry Tirri* email: University

More information

We are strong in research and particularly noted in software engineering, information security and privacy, and humane gaming.

We are strong in research and particularly noted in software engineering, information security and privacy, and humane gaming. Computer Science 1 COMPUTER SCIENCE Office: Department of Computer Science, ECS, Suite 379 Mail Code: 2155 E Wesley Avenue, Denver, CO 80208 Phone: 303-871-2458 Email: info@cs.du.edu Web Site: Computer

More information

arxiv: v3 [cs.cl] 7 Feb 2017

arxiv: v3 [cs.cl] 7 Feb 2017 NEWSQA: A MACHINE COMPREHENSION DATASET Adam Trischler Tong Wang Xingdi Yuan Justin Harris Alessandro Sordoni Philip Bachman Kaheer Suleman {adam.trischler, tong.wang, eric.yuan, justin.harris, alessandro.sordoni,

More information

Georgetown University at TREC 2017 Dynamic Domain Track

Georgetown University at TREC 2017 Dynamic Domain Track Georgetown University at TREC 2017 Dynamic Domain Track Zhiwen Tang Georgetown University zt79@georgetown.edu Grace Hui Yang Georgetown University huiyang@cs.georgetown.edu Abstract TREC Dynamic Domain

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

SELF-STUDY QUESTIONNAIRE FOR REVIEW of the COMPUTER SCIENCE PROGRAM

SELF-STUDY QUESTIONNAIRE FOR REVIEW of the COMPUTER SCIENCE PROGRAM Disclaimer: This Self Study was developed to meet the goals of the CAC Session at the 2006 Summit. It should not be considered as a model or a template. ABET Computing Accreditation Commission SELF-STUDY

More information

Master of Philosophy. 1 Rules. 2 Guidelines. 3 Definitions. 4 Academic standing

Master of Philosophy. 1 Rules. 2 Guidelines. 3 Definitions. 4 Academic standing 1 Rules 1.1 There shall be a degree which may be awarded an overall grade. The award of the grade shall be made for meritorious performance in the program, with greatest weight given to completion of the

More information

2016 Annual Report to the School Community

2016 Annual Report to the School Community 2016 Annual Report to the School Community Toorak Primary School School Number: 3016 Name of School Principal: Name of School Council President: Date of Endorsement: Julie Manallack Michelle Osborne All

More information

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,

More information

Skillsoft Acquires SumTotal: Frequently Asked Questions. October 2014

Skillsoft Acquires SumTotal: Frequently Asked Questions. October 2014 Skillsoft Acquires SumTotal: Frequently Asked Questions October 2014 1. What have we announced? Skillsoft has completed the previously announced acquisition of SumTotal. Skillsoft s acquisition of SumTotal

More information

Challenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley

Challenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling

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

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

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

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17. Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link

More information

Coaching Others for Top Performance 16 Hour Workshop

Coaching Others for Top Performance 16 Hour Workshop Coaching Others for Top Performance 16 Hour Workshop Content & Outcomes The Coaching Others for Top Performance workshop explores The Principles and Qualities of Genuine Leadership and focuses on developing

More information

Lip Reading in Profile

Lip Reading in Profile CHUNG AND ZISSERMAN: BMVC AUTHOR GUIDELINES 1 Lip Reading in Profile Joon Son Chung http://wwwrobotsoxacuk/~joon Andrew Zisserman http://wwwrobotsoxacuk/~az Visual Geometry Group Department of Engineering

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

DOCTOR OF PHILOSOPHY HANDBOOK

DOCTOR OF PHILOSOPHY HANDBOOK University of Virginia Department of Systems and Information Engineering DOCTOR OF PHILOSOPHY HANDBOOK 1. Program Description 2. Degree Requirements 3. Advisory Committee 4. Plan of Study 5. Comprehensive

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

Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science

Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science Gilberto de Paiva Sao Paulo Brazil (May 2011) gilbertodpaiva@gmail.com Abstract. Despite the prevalence of the

More information

IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University

IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University 06.11.16 13.11.16 Hannover Our group from Peter the Great St. Petersburg

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

Development of an IT Curriculum. Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008

Development of an IT Curriculum. Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008 Development of an IT Curriculum Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008 Curriculum A curriculum consists of everything that promotes learners intellectual, personal,

More information

Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task

Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task Stephen James Dyson Robotics Lab Imperial College London slj12@ic.ac.uk Andrew J. Davison Dyson Robotics

More information

arxiv: v1 [cs.cv] 10 May 2017

arxiv: v1 [cs.cv] 10 May 2017 Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University

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

Discriminative Learning of Beam-Search Heuristics for Planning

Discriminative Learning of Beam-Search Heuristics for Planning Discriminative Learning of Beam-Search Heuristics for Planning Yuehua Xu School of EECS Oregon State University Corvallis,OR 97331 xuyu@eecs.oregonstate.edu Alan Fern School of EECS Oregon State University

More information

TD(λ) and Q-Learning Based Ludo Players

TD(λ) and Q-Learning Based Ludo Players TD(λ) and Q-Learning Based Ludo Players Majed Alhajry, Faisal Alvi, Member, IEEE and Moataz Ahmed Abstract Reinforcement learning is a popular machine learning technique whose inherent self-learning ability

More information

Ecole Polytechnique Fédérale de Lausanne EPFL School of Computer and Communication Sciences IC. School of Computer and Communication Sciences

Ecole Polytechnique Fédérale de Lausanne EPFL School of Computer and Communication Sciences IC. School of Computer and Communication Sciences Ecole Polytechnique Fédérale de Lausanne EPFL School of Computer and Communication Sciences IC 1 WELCOME to the Master programs in Computer Science, Data Science and Communication Systems 2 TODAY S SPEAKERS

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

Student Handbook. This handbook was written for the students and participants of the MPI Training Site.

Student Handbook. This handbook was written for the students and participants of the MPI Training Site. Student Handbook This handbook was written for the students and participants of the MPI Training Site. Purpose To enable the active participants of this website easier operation and a thorough understanding

More information

Please find below a summary of why we feel Blackboard remains the best long term solution for the Lowell campus:

Please find below a summary of why we feel Blackboard remains the best long term solution for the Lowell campus: I. Background: After a thoughtful and lengthy deliberation, we are convinced that UMass Lowell s award-winning faculty development training program, our course development model, and administrative processes

More information

November 17, 2017 ARIZONA STATE UNIVERSITY. ADDENDUM 3 RFP Digital Integrated Enrollment Support for Students

November 17, 2017 ARIZONA STATE UNIVERSITY. ADDENDUM 3 RFP Digital Integrated Enrollment Support for Students November 17, 2017 ARIZONA STATE UNIVERSITY ADDENDUM 3 RFP 331801 Digital Integrated Enrollment Support for Students Please note the following answers to questions that were asked prior to the deadline

More information

UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak

UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS Heiga Zen, Haşim Sak Google fheigazen,hasimg@google.com ABSTRACT Long short-term

More information

NEURAL DIALOG STATE TRACKER FOR LARGE ONTOLOGIES BY ATTENTION MECHANISM. Youngsoo Jang*, Jiyeon Ham*, Byung-Jun Lee, Youngjae Chang, Kee-Eung Kim

NEURAL DIALOG STATE TRACKER FOR LARGE ONTOLOGIES BY ATTENTION MECHANISM. Youngsoo Jang*, Jiyeon Ham*, Byung-Jun Lee, Youngjae Chang, Kee-Eung Kim NEURAL DIALOG STATE TRACKER FOR LARGE ONTOLOGIES BY ATTENTION MECHANISM Youngsoo Jang*, Jiyeon Ham*, Byung-Jun Lee, Youngjae Chang, Kee-Eung Kim School of Computing KAIST Daejeon, South Korea ABSTRACT

More information

The A2iA Multi-lingual Text Recognition System at the second Maurdor Evaluation

The A2iA Multi-lingual Text Recognition System at the second Maurdor Evaluation 2014 14th International Conference on Frontiers in Handwriting Recognition The A2iA Multi-lingual Text Recognition System at the second Maurdor Evaluation Bastien Moysset,Théodore Bluche, Maxime Knibbe,

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

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

Adaptive learning based on cognitive load using artificial intelligence and electroencephalography

Adaptive learning based on cognitive load using artificial intelligence and electroencephalography Adaptive learning based on cognitive load using artificial intelligence and electroencephalography Håkon Jarle Hassel Master of Science in Computer Science Submission date: June 2017 Supervisor: Asbjørn

More information

There are some definitions for what Word

There are some definitions for what Word Word Embeddings and Their Use In Sentence Classification Tasks Amit Mandelbaum Hebrew University of Jerusalm amit.mandelbaum@mail.huji.ac.il Adi Shalev bitan.adi@gmail.com arxiv:1610.08229v1 [cs.lg] 26

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

Parsing of part-of-speech tagged Assamese Texts

Parsing of part-of-speech tagged Assamese Texts IJCSI International Journal of Computer Science Issues, Vol. 6, No. 1, 2009 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 28 Parsing of part-of-speech tagged Assamese Texts Mirzanur Rahman 1, Sufal

More information

First Line Manager Development. Facilitated Blended Accredited

First Line Manager Development. Facilitated Blended Accredited First Line Manager Development Facilitated Blended Accredited Why is First Line Manager development so critical? We combine The Oxford Group s expertise in leadership & management development and experienced

More information

What is an internship?

What is an internship? What is an internship? An internship or work placement is an important opportunity to gain working experience in a particular career area. There are generally two types of internship that are available,

More information

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,

More information

WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT

WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT PRACTICAL APPLICATIONS OF RANDOM SAMPLING IN ediscovery By Matthew Verga, J.D. INTRODUCTION Anyone who spends ample time working

More information

University of Cambridge: Programme Specifications POSTGRADUATE ADVANCED CERTIFICATE IN EDUCATIONAL STUDIES. June 2012

University of Cambridge: Programme Specifications POSTGRADUATE ADVANCED CERTIFICATE IN EDUCATIONAL STUDIES. June 2012 University of Cambridge: Programme Specifications Every effort has been made to ensure the accuracy of the information in this programme specification. Programme specifications are produced and then reviewed

More information

Dropout improves Recurrent Neural Networks for Handwriting Recognition

Dropout improves Recurrent Neural Networks for Handwriting Recognition 2014 14th International Conference on Frontiers in Handwriting Recognition Dropout improves Recurrent Neural Networks for Handwriting Recognition Vu Pham,Théodore Bluche, Christopher Kermorvant, and Jérôme

More information

COMPUTER-AIDED DESIGN TOOLS THAT ADAPT

COMPUTER-AIDED DESIGN TOOLS THAT ADAPT COMPUTER-AIDED DESIGN TOOLS THAT ADAPT WEI PENG CSIRO ICT Centre, Australia and JOHN S GERO Krasnow Institute for Advanced Study, USA 1. Introduction Abstract. This paper describes an approach that enables

More information

Welcome to the session on ACCUPLACER Policy Development. This session will touch upon common policy decisions an institution may encounter during the

Welcome to the session on ACCUPLACER Policy Development. This session will touch upon common policy decisions an institution may encounter during the Welcome to the session on ACCUPLACER Policy Development. This session will touch upon common policy decisions an institution may encounter during the development or reevaluation of a placement program.

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

TEACHING AND EXAMINATION REGULATIONS PART B: programme-specific section MASTER S PROGRAMME IN LOGIC

TEACHING AND EXAMINATION REGULATIONS PART B: programme-specific section MASTER S PROGRAMME IN LOGIC UNIVERSITY OF AMSTERDAM FACULTY OF SCIENCE TEACHING AND EXAMINATION REGULATIONS PART B: programme-specific section Academic year 2017-2018 MASTER S PROGRAMME IN LOGIC Chapter 1 Article 1.1 Article 1.2

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