Download as PDF, PPTX
Deep Learning - A hands-on introduction to image classification
- 1. Deep Learning – A hands-onintroduction to image classification Immanuel Weber, University of Applied Sciences Koblenz 2019/05/21
- 2.
- 6.
- 7.
- 8. (patrykchrabaszcz.github.io/Imagenet32) ImageNet is datasetof more than 14M images and more than 20k categories
- 9.
- 10.
- 11.
- 12. AlexNet: 60M parameters ResNet-34:21M parameters
- 13. Thanks to thegamers we got… (LAN Party: wikipedia.org)
- 14. NVIDIA DGX-2 (nvidia.com)(TeslaPerformance: nvidia.com)
- 15. Let’s classify beerand pizza… (Pizza, Beer: wikipedia.org)
- 16. sigmoid ReLU w1 w2 w3 Σ + b Single artificialneuron pizza? color size shape
- 17. color size shape pizza beer Slighlty simplified, asbiases and activation functions are omitted Simple neural network
- 18. x W1 W2W3 y Slighlty simplified, as biases and activation functions are omitted Simple neural network
- 19. x W1 W2W3 y • • • = Slighlty simplified, as biases and activation functions are omitted Simple neural network
- 20. Deep neural networksare just networks with many layers.
- 21.
- 22.
- 23.
- 24.
- 25.
- 26. weights, biases andkernels are unkown.
- 27. 0.8 0.2 (Pizza: wikipedia.org) Beer &Pizza Classifier
- 28. 0.55 0.45 (Beer: wikipedia.org) Beer &Pizza Classifier
- 29. inputs labels/targets Beer Pizza Beer Pizza Pizza Pizza Beer Beer Beer … Beer & PizzaDataset (Pizzas, Beers: wikipedia.org)
- 30. (Pizzas, Beers: wikipedia.org) Beer& Pizza Classifier Pizza Beer Beer Pizza Beer Pizza Pizza Pizza Beer … outputs labels Beer Pizza Beer Pizza Pizza Pizza Beer Beer Beer … loss function Simplified: outputs are not discrete labels Backpropagation + optimizer
- 31. Iterating over datasetsand optimizing values still takes a long time
- 32. Don‘t start fromthe beginning…
- 33. use pretrained networks anddo transfer learning.
- 34.
- 35.