Machine Learning Meetup SOF: Intro to ML

MACHINE LEARNING TRENDS

Machine Learning Meetup, Sofia

OPENING MEETUP

What to expect?

open format

exchange knowledge/ideas

everyone can be on stage

be tolerant, respect the others

COGNITIVE COMPUTING

“A cognitive computer combines

artificial intelligence and machine-

learning algorithms, in an approach which

attempts to reproduce the behavior of the

human brain.”

Wikipedia

COGNITIVE COMPUTING

addresses complex situations that are

characterized by ambiguity and

uncertainty;

handles human kinds of problems

COGNITIVE COMPUTING

“The smart machine era will be the most

disruptive in the history of IT.”

Gartner

COGNITIVE COMPUTING

“By 2018 half of all consumers will interact

with services based on cognitive computing

on a regular basis.”

IDC

COGNITIVE COMPUTING

Why now?

Advances in enabling technology

Increasingly large complex datasets

Emerging Platforms – Cloud, Mobile, Big

Data, Analytics, Social

COGNITIVE COMPUTING

Enabling Technologies

Natural Language Processing

Semantic Analysis

Informational Retrieval

Automated Reasoning

Machine Learning / AI

TRENDS

Computers That Learn

Computers That Think

Computers That Interact with Humans

Computers That Interact with Computers

Research and Use Cases

Education and Training

TRENDS

Siri, Google Now, Cortana

Workplace Disruption

Industry Transformation

Window of Opportunity

ML Practically Means

Algorithms that can learn from and make

predictions on data

Building a model from example inputs in

order to make data-driven predictions or

decisions

ML Broad Categories

Supervised learning

Unsupervised learning

Reinforcement learning

ML Tasks by Desired Output

Classification (typically supervised)

Regression (typically supervised)

Clustering (typically unsupervised)

Density estimation

Dimensionality reduction

ML Approaches

Decision tree learning Artificial neural networks (ANN) Support vector machines (SVM) Clustering Bayesian networks Sparse dictionary learning Genetic algorithms

What We at Imagga Do

Image classification (supervised learning)

Use ANN

More precisely - Deep Learning

Even more precisely - CNN (not the TV

station)

Convolutional Neural Networks (CNN)

Get raster data as input

Typically deep networks - multiple

convolutional and hidden layers

Very useful for images - the convolution

parameters are produced as a result of the

learning

Why NOW

GPUs have thousands of cores

Big amount of data, lots of data sources

Affordable utility computing (e.g. AWS, Azure,

Google Cloud)

Demand for ML solutions

Challenges

Very data greedy

Requires clean data

Requires data variety

Still takes a lot of time (1-4 weeks until the

model converges)

Solutions

Data augmentation (increase robustness)

Auto-cleaning of data (remove outliers and re-

train)

Designing the model architecture for multiple

GPUs

Topic for Next Meetup?

Overview/Presentations of the Bulgarian

companies using ML

Commercial applications and use-cases

Open-source software packages for ML

other . . .

MACHINE LEARNING RESOURCES

IMAGGA blog - www.imagga.com/blog/

ML Flipboard - http://bit.ly/1GYL65j

IR Flipboard - http://bit.ly/1IkyOPA

Applied Deep Learning for Computer Vision

with Torch - http://torch.ch/docs/cvpr15.html

DIY Deep Learning: a Hands-On Tutorial with

Caffe - https://github.com/BVLC/caffe

QUESTIONS

Q & A