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Better Machine Learning
- 2. Today’s Speakers Introduction Artificial Intelligence Manager, Mathematician, graduated at Scuola Normale of Pisa, after a specialization in logic he switched to management consulting in Boston Consulting Group where, passing through various practices and industries, he developed a strong passion for solving problems within complex organizations. From 7 years in the IT world he achieved challenging goals leading large teams through complex and dynamic environments. Alessandro Maserati Engineer with a brilliant university career, after being a founding member of a Social Commerce startup, Nicola Casamassima has enriched his professional experience taking part to the development of innovative FinTech systems for a Swiss leader of a field of finance. Now he is a Logolian AI expert that leads the development of strategic solutions using neural networks. Nicola Casamassima Main Address Via A. Volta 16, Chiasso (Ticino) Phone Number 0041 (0) 91 210 5827 Company E-mail Address info@logol.com Fax Number Are you kidding? 2www.logol.com
- 3. Agenda 3 • Which goals has Artificial Intelligence reached ? • What Is Artificial Intelligence ? • How Does it Works Machine Learning ? • How Does It Works a Neural Network ? • Which Neural Networks are available today ? • How to Train a Neural Network better than you ? www.logol.com
- 4. In Last Few Years AI Has Beaten All the Expectation 2016 2017 2018
- 5. AI Power is increasing by a factor of 10 per year
- 8. In a Complex System Each Level Provide a Completely Different Point of View
- 9. AI is Blue Google What is AI ? 9
- 10. AI Is What Can Solve a Non-Algorithmic Task www.logol.com 10 Neural Networks Machine Learning Artificial Intelligence CNN RNN GAN Deep Learning
- 11. AI can also hide huge problems!
- 12. It is not possible to find a black cat in a dark room if the cat is not there A correct training allows you to cut down on time and to remedy to your own mistakes. The AI model should not represent the problem but should allow to learn the solution Data Training StrategyModel
- 13. Each Component Is Critical To Succeed 13 Training Time Efficacy Data Quantity Data Quality Learning Strategy Model www.logol.com Few Data Wrong Strategy Poor Data Wrong Model
- 14. Wrong Data Compromise Whichever Goal 14 • Relevant Features • Correlation vs Casualization • Rare events • Survivor Bias • Real chaotic phenomenon • Evolutive Patterns www.logol.com
- 15. The Right Model Depend From Your Goal www.logol.com 15
- 17. Ok, Now Lets Talk About Neural Network 17www.logol.com But first, forget what you think to know about it 1. Neural networks are not models of the human brain 2. Neural networks are not just a “weak form” of statistics 3. Neural networks come in many different architectures 4. Size matters, but bigger isn’t always better 5. Many training algorithms exist for neural networks 6. Neural networks do not always require a lot of data 7. Neural networks cannot be trained on any data 8. Neural networks may need to be retrained 9. Neural networks are not hard to implement 10. Neural networks are not black boxes
- 18. How does a Neural Network work? and why normalization is so important? 18www.logol.com w1
- 19. How does a Neural Network learn? Machines know how to “learn from their own errors” with loss function, backpropagation, adaptive learning rate and mini-batch 19www.logol.com w1
- 20. How Can We Fill All These Nodes? 1. They have an easy derivative 2. They are well define in (-1,+1) 3. They discriminate around 0 www.logol.com 20
- 21. Let’s See Some Wonderful Design www.logol.com 21
- 22. Let’s See Some Wonderful Design www.logol.com 22
- 23. Inside Structure Could Be as Complex as you Want And how many different things they can do? 23www.logol.com
- 24. How Many Things Recurrent NN Can Do? And how many different purposes can they have? + GRU 24www.logol.com
- 25. How does a Convolutional Neural Network Work? Pics e non solo 25www.logol.com
- 26. You Can Start From The Cutting Hedge Result 26www.logol.com
- 27. Compression Is The Ultimate Comprehension Neural Networks could learn which aspects are really important to define a subject 27www.logol.com
- 28. Neural Networks are not Black Boxes I told you so!
- 29. Neural Networks Alone Do It Better 29www.logol.com
- 30. Let’s go see a demo
- 31. Generative Adversarial Networks Generate handwritten digits using MNIST dataset 31www.logol.com
- 33. The Training Process is Iterative on the Combined Network www.logol.com 33
- 34. Let’s Network Fight for Us The generator network is evaluated by the discriminator one Discriminator Layer fully connected with Leak ReLU as Activation Function and Batch Normalization after output 34www.logol.com Cross Entropy on final output will drive the training of the first NN MNISTGenerator Input features, tipically a noise array that randomize the network behavior
- 35. Let’s Start to Code www.logol.com 35
- 37. We Can Use Their Power Against Them 37www.logol.com
- 38. What impacts will AI cause over the next 5 years? AI AI AI AI AI AI AI AI AI AI
- 39. Thanks for Your Attention www.logol.com