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)
30. Detecting the input
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