What is the intuition behind backpropagation

Bachelor seminar Neural Networks SS 19 topic pool

Recurrent neural networks

Recurrent neural networks Gregor Mitscha-Baude May 9, 2016 Motivation standard neural network: Fixed dimensions of input and output! Motivation Variable input / output length in many applications. voice recognition

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Seminar: Machine Learning and Deep Learning

Seminar: Machine Learning and Deep Learning Summer Semester 2018 Prof. Dr. Xiaoyi Jiang, Sören Klemm, Aaron Scherzinger Institute for Computer Science, Working Group Pattern Recognition and Image Analysis (PRIA)

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Artificial Neural Networks

Faculty of Computer Science, Institute for Technical Computer Science, Professorship for VLSI Design Systems, Diagnostics and Architecture Artificial Neural Networks Advanced Seminar Martin Knöfel Dresden, November 16, 2017 Outline

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Deep Learning Prof. Dr. E. Rahm and co-workers

Deep Learning Prof. Dr. E. Rahm and employee seminar, winter semester 2017/18 Big data analysis pipeline data integration / enrichment data extraction / cleaning data acquisition data analysis interpretation volume

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Knowledge discovery in databases

Knowledge discovery in databases Deep Learning (II) Nico Piatkowski and Uwe Ligges Computer Science Artificial Intelligence 07/25/2017 1 of 14 Overview of folding networks Dropout Autoencoder Generative Adversarial

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Artificial intelligence

June 1st, 2017 Artificial Intelligence State of Research, Current Problems & Challenges Prof. Dr. Roland Kwitt Department of Computer Science University of Salzburg Overview of terms

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Deep learning for automatic document analysis

Deep learning for automatic document analysis apl. Prof. Marcus Liwicki DIVA Group, University of Friborg MindGarage, University of Kaiserslautern [email protected] www.mindgarage.de You can

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Deep learning to identify cooking processes

ZAFH Project: In cooperation with: Deep Learning to Recognize Cooking Processes M.Eng. Marco Altmann, Heilbronn University MATLAB Expo, Munich, June 21, 2018 ZAFH MikroSens project financed by the EU, EFRE

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Intelligent systems

Intelligent Systems Deep Learning Prof. Dr. R. Kruse C. Braune {rudolf.kruse, christian, braune} @ ovgu.de Institute for Intelligent Cooperating Systems Faculty for Computer Science Otto-von-Guericke University

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Neural Networks. Prof. Dr. Rudolf Kruse

Neural Networks Prof. Dr. Rudolf Kruse Computational Intelligence Institute for Intelligent Cooperating Systems Faculty of Computer Science [email protected] Christoph Doell, Rudolf Kruse Neural Networks

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Intelligence what is it?

Intelligence what is it? Intelligence (from Latin intellegere understand, literally choose between from Latin inter between and read casual, choose) is a collective term for the in psychology

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Neural Networks. Prof. Dr. Rudolf Kruse

Neural Networks Prof. Dr. Rudolf Kruse Computational Intelligence Institute for Intelligent Cooperating Systems Faculty of Computer Science [email protected] Christoph Doell, Rudolf Kruse Neural Networks

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Neural Networks. Prof. Dr. Rudolf Kruse

Neural Networks Prof. Dr. Rudolf Kruse Computational Intelligence Institute for Intelligent Cooperating Systems Faculty of Computer Science [email protected] Christoph Doell, Rudolf Kruse Neural Networks

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Knowledge discovery in databases

Knowledge Discovery in Databases Deep Learning Nico Piatkowski and Uwe Ligges Computer Science Artificial Intelligence 07/20/2017 1 of 11 Overview Artificial Neural Networks Motivation Formal Model Activation Functions

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Theoretical Computer Science 1

Theoretical Computer Science 1 Boltzmann machine David Kappel Institute for Fundamentals of Information Processing TU Graz SS 2014 Overview Boltzmann machine Neural networks The Boltzmann machine Gibbs

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Reinforcement learning

Reinforcement-Learning Lecture by: Fabien Lapok Supervisor: Prof. Dr. Meisel 1 Agenda Motivation Overview and problems of RL Current research My approach Conferences and sources 2 Reinforcement learning

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Introduction to Computational Linguistics

Introduction to Computational Linguistics Neural Networks WS 2014/2015 Vera Demberg Neural Networks What is it? One of the greatest advances in speech and image processing in recent years:

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Scalable deep learning. Big data, NLP, machine perception

Scalable Deep Learning Big Data, NLP, Machine Perception 2 Facebook: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour (2017) Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, Kurt Keutzer: ImageNet

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1 Introduction. 2 clustering

Learning vector quantization (LVQ) and K-Means-Clustering David Bouchain Proseminar Neural Networks Course-No .: CS4400 ISI WS 2004/05 [email protected] 1 Introduction The following is an overview

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Recurrent / feedback neural networks

Recurrent / feedback neural networks Research seminar Deep Learning 2018 University of Leipzig 01/12/2018 Lecturer: Andreas Haselhuhn Neural Networks Neuron consists of: Inputs sum function

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6.2 Feed-Forward Networks

6.2 Feed-Forward Networks We have seen that we can use neural networks consisting of one or more layers of perceptres to represent, for example, logical functions. Now let us consider

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Classification procedures and neural networks

Classification Methods and Neural Networks Advanced Seminar - Methods of Experimental Particle Physics Thomas Keck December 9, 2011 KIT University of the State of Baden-Württemberg and national research center

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Concepts of AI neural networks

Concepts of AI Neural Networks Franz Wotawa Institute for Information Systems, Database and Artificial Intelligence Group, Vienna University of Technology Email: [email protected] What are neural networks

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Introduction to NLP with deep learning

Introduction to NLP with Deep Learning Hans-Peter Zorn Minds mastering Machines, Cologne, April 26th, 2018 NLP is suddenly everywhere Summary aggregated reviews Document classification Translation Dialog systems

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YOUTH MEDIA 2.0 PROTECTION

YOUTH MEDIA 2.0 PROTECTION The challenge Why digital child protection? 51% of all 6-13 year olds have a telephone 86% of them chat regularly Source: KIM Study 2016 (https://www.mpfs.de/studien/kim-studie/2016/)

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Neural Networks. Christian Bohm.

Ludwig Maximilians University of Munich Institute for Computer Science Research Group Data Mining in Medicine Neural Networks Christian Böhm http://dmm.dbs.ifi.lmu.de/dbs 1 textbook for the lecture Textbook

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Overview of the topics

Overview of the topics 1. Description of natural terrain, especially underwater 2. Hand gesture recognition for mobile Augmented Reality (AR) applications 3. Deep learning in the field of face recognition

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Overview block lecture: Machine learning

Overview block lecture: Machine learning Table of contents Monday: 1. + 2. Learning unit 1. Overview and decision trees 1.1 Organizational aspects 1.2 Definition of machine learning 1.3 Classes of machine learning

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Artificial Neural Networks

Content (biological) neural networks Threshold value elements General neural networks Multi-layer perceptron Further types of neural networks 2 neural networks Consisting of many neurons (human

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Self-organizing cards

Self-organizing cards Introductory seminar Selected topics about agent systems 07/11/2017 Institute of Computer Science Self-organizing cards 1 Overview of motivation Self-organizing cards Structure &

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Institute for Artificial Intelligence

Institute for Artificial Intelligence Prof. Sebstaian Rudolph --- Computational Logic Prof. Steffen Hölldobler --- Knowledge Processing Prof. Ivo F. Sbalzarini --- Scientific Computing for Systems Biology

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Applications of the KI / SoSe 2018

Applications of the KI / SoSe 2018 Organizational Prof. Dr. Adrian Ulges Applied Computer Science / Media Informatics / Business Informatics / ITS Department DSCM University RheinMain Course Website: www.ulges.de

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