The full day workshop covers applied artificial intelligence through 7 morning and afternoon sessions. The morning sessions introduce AI concepts and provide hands-on coding exercises for machine learning applications, model deployment, and problem solving. After lunch, sessions focus on deep learning for image and text tasks, natural language processing, time series prediction, and generative models. The day concludes with a discussion of generative adversarial networks and style transfer through interactive coding exercises. Two AI researchers will lead the sessions and be available for questions.

1. Full Day of Applied AI
Morning
Session 1 Intro to Artificial Intelligence
09:00-09:45 Introduction to Applied AI
09:45-10:00 Coffee and break
Session 2 Live Coding a machine learning app
10:00-10:10 Getting your machine ready for machine learning
10:10-10.20 Training and evaluating the model
10.20-10.50 Improving the model
10.50-11.00 Coffee and break
Session 3 Machine learning in the wild - deployment
11:00-11.15 Coding exercise continued
11:15-11:45 Serving your own machine learning model | Code
11:45-11:55 How to solve problems | interactive exercise
11:55-12:00 Q and A
Lunch
12:00-13:00 Lunch
Afternoon
Session 4 Hello World Deep Learning (MNIST)
13:00-13:15 Deep Learning intro
13:00-13.15 Image recognition and CNNs | Talk |
13:15-13:45 Building your own convolutional neural network | Code |
13:45-14:00 Coffee and break
Session 5 Natural Language Processing
14:00-14.30 Natural language processing | Talk |
14:30-14:45 Working with language | Code |
14:45-15:00 Coffee and break
Session 6 Conversational interfaces and Time Series
14:00-14.20 Conversational interfaces
14:20-14:45 Time Series prediction
14:45-15:00 Coffee and break
Session 7 Generative models and style transfer
16:00-16.30 Generative models | Talk |
16:30-16:45 Trying out GANS and style transfer | Code |
16:45-17:00 Coffee and break
Anton Osika AI Research Engineer Sana Labs AB
anton.osika@gmail.com
Birger Moëll Machine Learning Engineer
birger.moell@gmail.com

2. What is AI?

3. What is AI?
The scope of AI is disputed: as machines become
increasingly capable, tasks considered as requiring
"intelligence" are often removed from the definition of AI, a
phenomenon known as the AI effect

4. What is AI?
If a computer system does something
and you think: “that was pretty smart”
-> AI

5. jj
Artificial Intelligence, Machine Learning and Deep Learning

6. AI hype and AI winters

7. AI > Human
● NLP

8. AI > Human
● NLP
● Image processing

9. AI > Human
● NLP
● Image processing

10. AI > Human
● NLP
● Image processing
● Decision making

11. AI > Human
● NLP
● Image processing
● Decision making

12. AI > Human
● NLP
● Image processing
● Decision making
● Autonomous system

13. AI > Human
● NLP
● Image processing
● Decision making
● Autonomous system

14. AI > Human
● NLP
● Image processing
● Decision making
● Autonomous system
● Surveillance of
complex systems

15. AI > Human
● NLP
● Image processing
● Decision making
● Autonomous system
● Surveillance of
complex systems
● Creativity ?

16. Supervised learning: x ↦ y
Examples:
● Image recognition. x = image, y = label (cat, dog, apple, ...)
● Predicting house prices. x = [neighborhood, m², ...], y = price
● Spam detection. x = email text, y = spam/not spam
● Speech recognition. x = audio, y = transcribed text

17. How does it work?

18. Stochastic Gradient Descent Training

19. Stochastic Gradient Descent Training

20. At each step the gradient of the error
between prediction and label is used to
determine how to change connections
towards a smaller error.
Stochastic Gradient Descent Training

21. Higher abstractions in later layers

22. Higher abstractions in later layers

23. Pre-trained models

24. Parts of machine learning

25. Applying Machine Learning (ML)

26. The ML Surprise - Effort Allocation

27. The ML Surprise - Effort Allocation

28. Most common pitfalls

29. 1. Evaluate simple heuristics without ML first
2. Use a ML API
3. Find a pre-trained open source model
4. Finetune a pre-trained model
5. Use open source code to train a model on your data
6. Build a model from scratch
Priority list when applying ML

30. Deep Learning vs other approaches
● Image recognition. x = image, y = label (cat, dog, apple, ...) Visual ✅
● Speech recognition. x = audio, y = transcribed text Audio ✅
● Text classification. x = email text, y = spam/not spam NLP ✅
● Predicting house prices. x = [neighborhood, m², ...], y = price Tabular❌

31. Deep Learning vs other approaches
Deep Learning:
● Image recognition. x = image, y = label (cat, dog, apple, ...) Visual ✅
● Speech recognition. x = audio, y = transcribed text Audio ✅
● Text classification. x = email text, y = spam/not spam NLP ✅
Decision trees, linear models, nearest neighbour:
● Predicting house prices. x = [neighborhood, m², ...], y = price Tabular❌

32. Deep Learning in action
RNNs predicting text continuation
RNNs predicting text continuation (simple)

33. Workflow for adding a ML feature
1. Define evaluation criteria and evaluation data
2. Find model with sufficient performance
3. Create container with web server, model and parameters
4. Deploy container in cloud
5. Send features over HTTP from main service
6. Receive predictions
7. ...profit $

34. Hierarchy of needs

35. Being productive with machine learning doesn’t
require a deep understanding of the mathematics

36. Recommended reading list
Applying Machine Learning
People + AI Guidebook - Google - build great products with ML
http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf - best practices when productionizing ML
Machine Learning Yearning - Andrew Ng - applied ML research strategy
Cloud provider APIs (e.g. AWS)
Advanced modelling
Deep Learning for Coders - fast.ai course
100 page ML book

37. Extra:How long to AI takeover?

42. Success stories

43. Success stories

44. ML Success stories