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Machine Learning Meetup SOF: Intro to ML


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Slides for Machine Learning Meetup in Sofia, June 2015. Basics of ML

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Machine Learning Meetup SOF: Intro to ML

  1. 1. MACHINE LEARNING TRENDS Machine Learning Meetup, Sofia
  2. 2. OPENING MEETUP What to expect? open format exchange knowledge/ideas everyone can be on stage be tolerant, respect the others
  3. 3. 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
  4. 4. COGNITIVE COMPUTING addresses complex situations that are characterized by ambiguity and uncertainty; handles human kinds of problems
  5. 5. COGNITIVE COMPUTING “The smart machine era will be the most disruptive in the history of IT.” Gartner
  6. 6. COGNITIVE COMPUTING “By 2018 half of all consumers will interact with services based on cognitive computing on a regular basis.” IDC
  7. 7. COGNITIVE COMPUTING Why now? Advances in enabling technology Increasingly large complex datasets Emerging Platforms – Cloud, Mobile, Big Data, Analytics, Social
  8. 8. COGNITIVE COMPUTING Enabling Technologies Natural Language Processing Semantic Analysis Informational Retrieval Automated Reasoning Machine Learning / AI
  9. 9. TRENDS Computers That Learn Computers That Think Computers That Interact with Humans Computers That Interact with Computers Research and Use Cases Education and Training
  10. 10. TRENDS Siri, Google Now, Cortana Workplace Disruption Industry Transformation Window of Opportunity
  11. 11. 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
  12. 12. ML Broad Categories Supervised learning Unsupervised learning Reinforcement learning
  13. 13. ML Tasks by Desired Output Classification (typically supervised) Regression (typically supervised) Clustering (typically unsupervised) Density estimation Dimensionality reduction
  14. 14. ML Approaches Decision tree learning Artificial neural networks (ANN) Support vector machines (SVM) Clustering Bayesian networks Sparse dictionary learning Genetic algorithms
  15. 15. What We at Imagga Do Image classification (supervised learning) Use ANN More precisely - Deep Learning Even more precisely - CNN (not the TV station)
  16. 16. 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
  17. 17. 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
  18. 18. Challenges Very data greedy Requires clean data Requires data variety Still takes a lot of time (1-4 weeks until the model converges)
  19. 19. Solutions Data augmentation (increase robustness) Auto-cleaning of data (remove outliers and re- train) Designing the model architecture for multiple GPUs
  20. 20. Topic for Next Meetup? Overview/Presentations of the Bulgarian companies using ML Commercial applications and use-cases Open-source software packages for ML other . . .
  21. 21. MACHINE LEARNING RESOURCES IMAGGA blog - ML Flipboard - IR Flipboard - Applied Deep Learning for Computer Vision with Torch - DIY Deep Learning: a Hands-On Tutorial with Caffe -
  22. 22. QUESTIONS Q & A