ML Session n°5

Transcript

1. ML: advanced neural networks April 2017

2. Recap

3. Artificial neuron: Perceptron

4. Artifical neuron : activation function

5. Artificial neuron -> Artificial neural network

6. Teaching a neural net

7. Demo: Tensorflow Playground

8. Demo: layered network, still the hard way (97%)

9. Dropout

10. Overfitting

11. Dropout

12. Dropout Result

13. Convolutional Neural Networks

14. Fully connected vs Convolution

15. Convolutional layer: different input sizes

16. Inside a convolution neuron: kernels

17. Stacked computational layers

18. “Deep” layers

19. 2D and multiple layers

20. Pooling

21. Max pooling

22. Max pooling in CNN

23. Pooling

24. Multiple layers

25. Convolutional classification (2012)

26. Convolutional architecture

27. Convolutional network -> fully connected

28. Convolutional success (2012)

29. None

30. Detecting the input

31. None

32. Sliding a window

33. Sliding window with different scales

34. Sliding window: result

35. Content to detect in the window for text

36. Sliding window on text

37. Detecting individual characters

38. Cheating

39. Image classification through caffe image = '/tmp/kitten.png' # preprocess the

    kitten and resize it to 224x224 pixels net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(image)) # make a prediction from the kitten pixels out = net.forward() # extract the most likely prediction print("Predicted class is #{}.".format(out['prob'][0].argmax()))

40. Mistakes

41. Finding the gradient def compute_gradient(image, intended_outcome): # Put the image

    into the network and make the prediction predict(image) # Get an empty set of probabilities probs = np.zeros_like(net.blobs['prob'].data) # Set the probability for our intended outcome to 1 probs[0][intended_outcome] = 1 # Do backpropagation to calculate the gradient for that outcome # and the image we put in gradient = net.backward(prob=probs) return gradient['data'].copy()

42. Using the gradient to tweak the image

43. Adding more gradients

44. Transforming a panda into a vulture

45. Panda gradient

46. Next Recurrent Neural Networks Unsupervised Learning Reinforcement Learning April 2017