5th Vienna Deep Learning Meetup
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1 5th Vienna Meetup 22 September Automic Software Hosts: Josef Puchinger, Thomas Lidy, Jan Schlüter
2 5th Vienna Meetup Agenda: Welcome (Thomas Lidy) & The Future of Automation (Josef Puchinger, Automic Software) Going Deeper with GoogLeNet and CaffeJS (Christoph Körner) Latest News / Hot topics (Jan Schlüter, Alex Schindler) Open discussions
3 Latest News Hot Topics a 5-10 min block at every meetup to briefly present trending topics Send us contributions (tom.lidy@gmail.com) or come with slides to do a 5-10 min block yourself!
4 Neural Networks Demystified Really nice introduction into Machine Learning / NN / DL / Data Preparation, Classification/Regression, etc. watch?v=bxe2t-v8xrs
5 Apache Singa new open source distributed deep learning platform by Apache v 1.0 released on 8 Sep 2016 flexible and scalable ( for big data analytics ) simple programming model making the distributed training process transparent to users training of DL models on a GPU cluster possible
6 Residual Network improvements Residual Network (Dec 2015): Allow deeper networks via skip connections. Skip connections add input to output. Thus, each layer learns to correct residual errors.
7 Residual Network improvements
8 Residual Network improvements Wide ResNet (May 2016): Make network wider (more filters), not deeper. Wide ResNet of 16 layers > original ResNet of 1001 layers.
9 Residual Network improvements DenseNet (Aug 2016): Similar to ResNet, but skip connections concatenate input to output instead of adding it. Each layer can access the outputs of all previous layers, and adds a few feature maps to the pool. SOTA on CIFAR10/100,
10 WaveNet ConvNet that can predict next step of time sequence, using a clever architecture for processing a large temporal context (about past time steps)
11 WaveNet Trained to generate samples of raw audio ( ms context) Without side inputs: example A example B With side inputs (phones, speaker id): example A example B example C Without side inputs, trained on piano YouTube videos: example A example B
12 Network Architecture Scale Invariance
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15 Upscale 16x16 images by factor 4 Deep Convolutional Generative Adversarial Network (DCGAN) Image as input instead of gaussian noise Loss function measures difference between input and scaled version Generator uses Residual Network (ResNet) modules Code on Github Actual DNN Output Interpolation Input Image super-resolution through deep learning
16 Paper: Github:
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19 Ground Truth
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22 Pre-Trained Models now available in Lasagne and Keras
23 Pretrained models 5-min Quick-Test Using Python and Keras/Theano
24 DL for Sound Recognition Recent IEEE AASP DCASE Workshop in Budapest Urban audio scene classification (bus, metro, tram, park, ) Audio event detection in a stream (keyboard, door, phone ) Domestic audio tagging (child, male, female, TV, game, household) -> many used CNN/DNN JKU Linz winner of Audio scene task TU Wien winner of Domestic audio task Data + algorithm descriptions available:
25 Announcements
26 Call for Meetup Topics / Talks Looking for: Industry Applications of DL Real World Use Cases of DL Interesting novel scientific results Niche applications Latest News / Hot topics Get in touch if you want to contribute something! tom.lidy@gmail.com
27 Job Offer in DL Startup Mon Style is a tech startup that uses machine-learning to deliver highly accurate results to online shoppers. Their mission is to revolutionize the way we discover, interact and shop products. Job Profile: Full time position based in Vienna Flexible working hours and home office Knowledge of algorithms and data structures, Python Knowledge of machine learning (Classification, Regression and Clustering) Experience using deep learning frameworks like Caffe/Torch/Theano/TensorFlow + numpy, pandas etc. Application: office@mon-style.com
28 BitCoin Analytics Funded MSC Thesis Analyzing Bitcoin transactions Fraud Detection Anomaly Detection Large Scale Data Analysis Contact: and
29 Waves Music Hackday Fri 30 Sep: Kick-Off + Tool Presentation Sat 1 Oct: Hacking! Register!
30 netidee Open Source Community Camp
31 Thanks a lot to Automic Software for hosting us!
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