Novi sad ai event 2-2018
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Novi Sad AI 2.0 event, we talked about Deep learning in field of Face verification and Game control.
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Novi sad ai event 2-2018
- 1. 30.05.2018
- 3. There has been a 14X increase in the number of active AI startups since 2000 The share of jobs requiring AI skills has grown 4.5X since 2013.
- 4. NOVI SAD APPLIED INTELLIGENCE COMMUNITY City.AI NOVI SAD shaping up around a community whose goal are : To help local actors develop efficiently the Serbian branch on AI internationally To work around applied AI challenges with the local & global ecosystem actors To democratize AI innovation and close the gap between technology and society To train and challenge the local community
- 5. LEVERAGING THE POTENTIAL OF AI IN 50+ CITIES AFRICA Accra - Lagos ASIA Bangalore - Bangkok - Beirut - Chiang Mai - Hanoi - Hong Kong - Jakarta - Johor Bahru - Karachi - Lahore - Manila - Pune - Seoul - Singapore - Taipei AUSTRALASIA Wellington EUROPE Amsterdam - Berlin - Bratislava - Bristol - Brussels - Bucharest - Budapest - Cambridge - Cluj - Cologne - Copenhagen - Hamburg - Iasi - Krakow - Kyiv - London - Madrid - Munich - Novi Sad - Oxford - Paris - Sofia - Stockholm - Stuttgart - Tallinn - Tirana - Valencia - Valletta - Vienna - Vilnius NORTH AMERICA Austin - LA - New York - San Diego - San Francisco SOUTH AMERICA Bogota - La Paz - Sao Paulo
- 6. Our team NOVI SAD AI TEAM Jovan Stojanovic Ambassador of Novi Sad-AI Marko Jocic Co-Ambassador of Novi Sad-AI Jovana Miletic Operation manager of Novi Sad-AI
- 7. Ilija Radulovic Software developer on CNN for Face Recognition Dusan Josipovic Machine learning engineer on CNN for learning game Brakeout LESSONS LEARNED BY
- 8. Face Verification using Deep Neural Networks
- 9. Think big, start small, grow fast. Be pragmatic. Smart Start
- 10. • What happened? • Why it happened? • How it happened? Data Analysis
- 13. •True Positive Rate (TPR) •False Positive Rate (FPR) •Measure TPR @ various FPR (10e-3, 10e-4, 10e-6) Metrics
- 14. Training/Testing Pipeline, Detailed • Dataset cleaning filter (remove mislabeled data) • Black list filter • Minimal number of images per class (person) Filtering (remove certain classes/images) • Alignment • Data Augmentation Preprocessing (fix/modify images) • Central loss, Softmax loss • ResNet architecture Training (create model) • Protocols: 1-1, 3-1 • Metrics calculation • Error analysis Testing/Validation (test and analyze model)
- 15. • Text files with internal format as input/output • Easy to plugin into pipeline flow at any place • Easy to reproduce experiments and each step Training/Testing Pipeline (Scripts)
- 16. • Training set labeling is inaccurate. Example images for one class (person): Preprocessing: Cleaning Before After
- 18. TD1 •10,575 subjects and 494,414 images TD2 •99,892 subjects and 8,456,240 images Training Datasets
- 19. Azure machines with GPUs Tensorflow, Python 250 epochs, 1000 iterations per epoch TD1 4-5 days TD2 7 days Model size: 300-500MB disk size Training history within Tensorboard Training
- 20. Embeddings Input Image Preprocess Deep Neural Network Detect Face & Align Detect Blur Detect Head pose Detect Illumination Detect Glasses … Embeddings
- 21. • Small intra-class distance • Large inter-class distance Embeddings: Goal
- 23. Compute embeddings with trained model Compute distances Compute metrics Error analysis Plot diagrams Testing
- 24. Testing Results: Class Histograms
- 27. Illuminance Blurring Head pose Occlusion Why False Negatives
- 29. 60sec Story
- 30. “In around 60 seconds after opening the sale to the public, SingularityNET sold out of the whole whole amount of available tokens (the AGI token), bringing the total raised to $36 million.” 60sec Story
- 31. Think big, start small, grow fast. Be pragmatic. Smart Start
- 33. Ilija Radulovic Software developer on CNN for Face Recognition Dusan Josipovic Machine learning engineer on CNN for learning game Brakeout LESSONS LEARNED BY
- 34. Konvoluciona neuralna mreža za učenje igre Breakout
- 35. KOMPONENTE ◎ Neuralne mreže ◎ Okruženje Breakout ◎ Neuralni model ◎ Dilema istraži eksploatiši ◎ Memorija iskustava ◎ Proces treniranja ◎ Demo 35
- 36. IGRA BREAKOUT 36
- 38. Moderna VI 38
- 43. OKVIR KOJI ZAPAŽA AGENT 43 160 210 84 84
- 44. ISKUSTVO 44
- 49. Demo 49
- 51. Zaključak 51
- 52. Hvala! 52
- 53. Ilija Radulovic Software developer on CNN for Face Recognition Dusan Josipovic Machine learning engineer on CNN for learning game Brakeout QA WITH :
- 54. World Summit World Summit www.worldsummit.ai 10-11th of October 4500+ ATTENDEES 100+ COUNTRIES 140+ SPEAKERS 5+ CONTENT STREAMS
- 55. CEE region Summit and workshop www.cee.city.ai 9th of November Novi Sad, Fair center 350+ ATTENDEES 20+ COUNTRIES 5+ SPEAKERS 12 WORKSHOPS SEPARATED BY RETAIL,AGRICULTURE,AUTOMOTIVE AND FINANCE/HEALTCARE INDUSTY
- 56. Happy First event !
- 57. Folllow us at Novi Sad city AI