This document summarizes a machine learning meetup in Sofia. It discusses trends in cognitive computing and machine learning, including computers that learn, think, interact with humans and other computers. It also outlines enabling technologies for cognitive computing like natural language processing. Specific machine learning tasks like classification, regression and clustering are covered. Challenges in machine learning like data requirements and training time are addressed. The document promotes sharing knowledge and ideas at the open meetup format.

2. MACHINE LEARNING
TRENDS
Machine Learning Meetup, Sofia

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

5. COGNITIVE COMPUTING
addresses complex situations that are
characterized by ambiguity and
uncertainty;
handles human kinds of problems

6. COGNITIVE COMPUTING
“The smart machine era will be the most
disruptive in the history of IT.”
Gartner

7. COGNITIVE COMPUTING
“By 2018 half of all consumers will interact
with services based on cognitive computing
on a regular basis.”
IDC

8. COGNITIVE COMPUTING
Why now?
Advances in enabling technology
Increasingly large complex datasets
Emerging Platforms – Cloud, Mobile, Big
Data, Analytics, Social

9. COGNITIVE COMPUTING
Enabling Technologies
Natural Language Processing
Semantic Analysis
Informational Retrieval
Automated Reasoning
Machine Learning / AI

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

13. ML Broad Categories
Supervised learning
Unsupervised learning
Reinforcement learning

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

19. Challenges
Very data greedy
Requires clean data
Requires data variety
Still takes a lot of time (1-4 weeks until the
model converges)

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

23. QUESTIONS
Q & A