3. OPENING MEETUP
What to expect?
open format
exchange knowledge/ideas
everyone can be on stage
be tolerant, respect the others
4. 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
10. TRENDS
Computers That Learn
Computers That Think
Computers That Interact with Humans
Computers That Interact with Computers
Research and Use Cases
Education and Training
11. TRENDS
Siri, Google Now, Cortana
Workplace Disruption
Industry Transformation
Window of Opportunity
12. 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
14. ML Tasks by Desired Output
Classification (typically supervised)
Regression (typically supervised)
Clustering (typically unsupervised)
Density estimation
Dimensionality reduction
15. ML Approaches
Decision tree learning
Artificial neural networks (ANN)
Support vector machines (SVM)
Clustering
Bayesian networks
Sparse dictionary learning
Genetic algorithms
16. What We at Imagga Do
Image classification (supervised learning)
Use ANN
More precisely - Deep Learning
Even more precisely - CNN (not the TV
station)
17. 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
18. 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
20. Solutions
Data augmentation (increase robustness)
Auto-cleaning of data (remove outliers and re-
train)
Designing the model architecture for multiple
GPUs
21. Topic for Next Meetup?
Overview/Presentations of the Bulgarian
companies using ML
Commercial applications and use-cases
Open-source software packages for ML
other . . .
22. 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