Post on 19-Jan-2017
PRACTICAL INTRODUCTION TO
ARTIFICIAL INTELLIGENCEDEEP LEARNINGLARGE-SCALE IMAGE ANALYTICSKEVIN MADER / FLAVIO TROLESE4QUANT | BIG IMAGE ANALYTICS
PANTALKTUESDAY, MARCH 19 2016 / IMPACTHUB GARAGE ZURICH
4Quant | BIG IMAGE ANALYTICS
Die Länder, die Österreich umgeben.
↓
Was sind Schweiz, Italien, Slowenien, Ungarn, Tschechische Republik, Deutschland, Slovakei?
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
4Quant | BIG IMAGE ANALYTICS
http://4quant.com/javascript-breakout/
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HOW?
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STANDARD MACHINE LEARNING
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CORE IDEASWhat is an image?
What a human sees What a machine sees
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CORE IDEASFeature Generation → Making the computer see more
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CORE IDEASTraining / Validation
With all machine learning techniques it is critical to divide data into training and validation sets.
The algorithm can then be tested (validated) on data it has never seen before to ensure it generalizes
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OUTPUT / LOSS FUNCTION
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The in order for machine learning to work there has to be a single output for the system which quantifies how well it is working
- the number of correctly identified structures (true-positives)
- the number of correct letters in a sentences
- the score of a game
CORE IDEASLearning from tagged data (supervised)
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What is this?
PROBLEMSFeatures can be very difficult to ‘engineer’.
What makes a person a person?
More data doesn’t always lead to better results.
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DEEP LEARNING
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One of these can recognize without any programming by just experiencing and getting feedback.
THE IDEA
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https://flic.kr/p/2eryEj
The human brain is a large, layered, connected network of neurons.
THE IDEA
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https://flic.kr/p/5J4uci
We understand how some of these layers work and can make computationally fast models for simulating their behavior
THE IDEA
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DEEP LEARNING
Deep learning is a set of algorithms in machine learning that attempt to learn in multiple levels, corresponding to different levels of abstraction.
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THE IDEA
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A machine learning system with millions of inputs
And 1 output
THE IDEA
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The networks can get very large (hence the deep)
Here is the Inception Network from Google
TYPES OF ARTIFICIAL NEURONS
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Fully-connected → everything connected to everything
Convolutional (CNN) → mix things together
Recurring (RNN) → remember parts of sequences
Recurring Networks
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http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Recurring Networks
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http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Given the starting letter h
Predict the rest of the letters
SCHWIIZERDÜTSCH
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Goes through hundreds of pages of text character by character and trains neurons to predict the correct output
The text shows the algorithm learning to complete the sentence.
The curve shows how confident it is in each guess
SCHWIIZERDÜTSCH
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100 gu sisxt n eigeiua a esSWctaicobemhat,E out?s v t t uew
10K Uhe uf Hountigm don d’Bomura fürsyn al jerisim Sbeour Rucch
65K Übschamt wiänä wo und ebs haGscham, üblart uls zä flusch, zänsert. De Unner sindämzalagsel
100K Totatwärt. Dischtä Tittä vo dä ues und erwiä Gsacht agä schtüswongeilä. Beterischtiongehärne vordä em Verbichunt. Diä Mieräng ader h d Zientlichnig vu CHF
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SCHWIIZERDÜTSCH
Spell/Grammar Check (for a language with ‘no rules’) Dialect Detector
Autocomplete
APPLICATIONS
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Automatic C code
Wikipedia Text
BEYOND SCHWIIZERDÜTSCH
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CONVOLUTIONAL NETWORKS
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Pixels Edges Object parts Object models
→ → →
CONVOLUTIONAL NETWORKS
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CONVOLUTIONAL NETWORKS
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4Quant | BIG IMAGE ANALYTICS
CONVOLUTIONAL NETWORKS
Street, Trees, Fence, Bicycle
UNDERSTANDING COMPLEX SCENES
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Self-driving cars need to be able to identify walkways automatically
All point geo-referenced
IDENTIFY WALKWAYS
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Understanding what is happening inside of these complex networks
DREAMING
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Applying parts of trained networks to other types of images.
TRANSFER
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A challenging field
- noisy
- highly variable
- many tissues / diseases look the same
MEDICAL IMAGES
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Red are lungs
Yellow are bones
Blue are the other organs
MEDICAL IMAGES
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FINDING CANCER
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MEDICAL IMAGES
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Convolutional neurons act on the image and learn to extract the relevant information
MEDICAL IMAGES
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These representations can then be used to automatically find organs like the heart and measure blood flow
→ →
MEDICAL IMAGES
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These representations can then be used to automatically find organs like the heart and measure blood flow
→ →
Open Challenges
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You need a lot of data to identify (1K-100M)
Some networks learn well, others do not
Parameters can make a huge difference
Intermediate layers can be difficult to interpret
RESOURCESGoogle Cloud Vision APIIBM Watson Vision ServiceMicrosoft Project Oxford
TensorFlowCaffeMoodstocks
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PRACTICAL INTRODUCTION TO
ARTIFICIAL INTELLIGENCEDEEP LEARNINGLARGE-SCALE IMAGE ANALYTICSTHANK YOUKEVIN MADER / FLAVIO TROLESE4Quant Ltd.
PANTALKTHURSDAY MARCH 19 2016 / IMPACTHUB GARAGE ZURICH
4Quant| BIG IMAGE ANALYTICS