! References: ! Computer eyesight gets a lot more accurate, NY Times. ! Stanford CS 231n. ! Christopher Olah s blog. ! Take ECS 174!
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1 Exams ECS 189 WEB PROGRAMMING! If you are satisfied with your scores on the two midterms, you can skip the final! As soon as your Photobooth and midterm are graded, I can give you your course grade (so far) so you can decide! The material in this lecture will not be on the final. It covers the interesting part of our project. 6/5 Cloud Vision API the magic How does it work?! Sadly, it really is magic or at least not well understood!! But we have a word for it al Neural Networks!! References:! Computer eyesight gets a lot more accurate, NY Times! Stanford CS 231n! Christopher Olah s blog! Take ECS 174! Neural Networks Artificial Neuron! Program based on metaphor of how brains work! Highly interconnected collection of neurons! Input comes in on dendrites! When input is enough, neuron fires and sends signal through axon to other neurons! Possibly many inputs! Weights! Continuous φ function φ 1
2 Neural Network Image Labeling CAT! Much bigger; billions of connections! Different configurations! Deep learning -> many layers! Input is image pixels! Output is words Training Weight adjustment the chain rule!! Large training set of image-label pairs! Start with random weights! For each training image:! Adjust weights at every node to move the activation of the training label up and the other labels down! Do this over and over again until cycling through the whole training set does not change the weights any more! This takes a long, long time x 2 x 1 w 2 w 1 φ(w w1 1 x 1 + w 2 x x) O = output of whole network do/dw 1 = dx 3 /dw 1 do/dx 3 do/dx 1 = dx 3 /dx 1 do/dx 3 x 3 Weight adjustment the chain rule! Old idea x 1 w 1 w 2 φ (w w1 1 x 1 + w 2 x x) x 3 x 2 O = output of whole network do/dw 1 = dx 3 /dw 1 do/dx 3 do/dx 1 = dx 3 /dx 1 do/dx 3 Start at the output end, work your way back taking derivatives back propagation! McCulloch and Pitts, seminal paper, 1943! Rosenblatt, weights contain memory, 1958! Minsky and Pappert, critique, 1969! Rumelhart, Hinton and Willams, backprop,
3 So why is it a big thing now?! Use GPUs! Do it at Google in a massive distributed parallel environment (Tensorflow) So why is it a big thing now?! Big data! We have enough training data to get really big So why is it a big thing now?! Big data! We have enough training data to get really big! Better organization of networks! al neural network! is the mathematical operation for pattern matching! Say we wanted to find a face in a 1D signal! Use a face-shaped pattern. Multiply pattern by face at every point to get a score for pattern position.! Use a face-shaped pattern. Multiply pattern by face at every point to get a score for pattern position. 3
4 ! Best score a position where pattern and signal match the best!! Best score a position where pattern and signal match the best! of original image Patterns for images! Weights get multiplied by input x. So we will get strong signals (o j ) if weights form a pattern that matches part of the image (eg. kitty eyes)! Feed all small sub-images to a neuron, and hope we optimize weights so it makes an interesting max.! Create hundreds or thousands of pattern neurons, optimize each over all possible positions, look for peak outputs al Neural Network Automatically generated features! Image as input! consider features of small areas! Pooling layers select the strongest features! Fully connected layers connect features to outputs! Two sets of convolution features trained on two different GPUs, feeding into the same fully connected layer! One side looks for dark-to-light, edges, gradients, the other works on color and texture 4
5 Recognition in the browser Big data for image labeling! ImageNET training data! Li Fei-Fei (Stanford) and Kai Li (Princeton)! Training is slow but recognition is pretty fast (we are seeing seconds, including the round-trip to Google! A category for every English noun! Over a billion images! Massive data collection using Mechanical Turk over several years! Key factor that has enabled us to train CNNs for this task Other big Deep NN applications! Speech recognition! Machine translation! The great AI awakening NY Times! Text understanding and question answering (grammar, meaning) 5
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