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Image Classification with Deep Learning | DevFest + GDay, George Town, Malaysia 2017

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Virot "Ta" Chiraphadhanakul
Virot "Ta" Chiraphadhanakul
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Ta Virot Chiraphadhanakul, PhD (@tvirot) Google Developer Expert in Machine Learning 
 Managing Director, Skooldio Image Classification with Deep Learning
Source: https://xkcd.com/1425/
Source: http://cs231n.github.io/classification/
Not anymore! Source: https://xkcd.com/1425/
The Google self-driving car project became Waymo with a mission to make it easy and safe for people and things to move around Waymo Video: Waymo
A deep learning algorithm capable of interpreting signs of Diabetic Retinopathy (DR) in retinal photographs. 
 DR — an eye condition that affects people with diabetes — is the fastest growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide. Detecting Diabetic Eye Disease Photo: Google Blog
An artificial intelligence trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists.
 In this work, we pretrain a deep neural network at general object recognition, then fine-tune it on a dataset of ~130,000 skin lesion images comprised of over 2000 diseases. Identifying Skin Cancer Photos: Nature
"Farmers want to focus and spend their time on growing delicious vegetables.” — Makoto Koike Cucumber 
 Sorter Photos: Google Cloud Platform / Kaz Sato
Demo https://deeplearnjs.org/demos/teachable_gaming/ https://teachablemachine.withgoogle.com/
01 Intro to Deep Learning 02 Convolutional Neural Network (CNN) 03 Transfer Learning
Deep Learning A Quick Intro
Photo: NVIDIA Blog
x1 x2
x1 x2
Σ x1 x2 1 O input output A Perceptron
Σ x1 x2 1 weighted 
 sumw1 w2 b w1x1 + w2x2 + b bias A Perceptron
Ow1x1 + w2x2 + b Activation 
 Function -1 1 0 A Perceptron
Σ x1 x2 1 w1 w2 b O 1 if w1x1 + w2x2 + b > 0 -1 if w1x1 + w2x2 + b < 0 A Perceptron
x1 + x2 > 0 x1 x2 x1 + x2 = 0 x1 + x2 < 0
http://playground.tensorflow.org/
Other Activation Functions Source: http://introtodeeplearning.com/ (Lecture 1)
x1 x2 h11 x3 input layer hidden layers h12 O1 output layer h21 h22 Deep Neural Network
http://playground.tensorflow.org/
x1 x2 x12 + x22 = 9 x12 + x22 > 9 x12 + x22 < 9
http://playground.tensorflow.org/
http://playground.tensorflow.org/
https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/
https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/
https://www.tensorflow.org/get_started/mnist/beginners Red represents negative weights, while blue represents positive weights. See more: scs.ryerson.ca/~aharley/vis/fc
Challenges Doesn’t scale e.g., 300 x 300 RGB image would require 270,000 weights for each neuron in the first hidden layer of the neural network Easily overfit
CNN Convolutional Neural Network
• Apply filters to one small region at a time to detect certain features of an object (edge, circle, or certain shapes) • A feature of an object is translation invariant, and the filters applied to each small region can share weights/parameters • Fewer parameters required. More robust. Convolutional Neural Network (CNN)
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov 1998. Convolutional Neural Network (CNN)
Visualizing and Understanding Convolutional Networks (Zeiler and Fergus, 2014)
1. Convolutional Layer 2. Pooling Layer 3. Fully Connected Layer Layers in CNNs Source: http://cs231n.github.io/convolutional-networks/ INPUT 32x32 Convolutions SubsamplingConvolutions C1: feature maps 6@28x28 Subsampling S2: f. maps 6@14x14 S4: f. maps 16@5x5 C5: layer 120 C3: f. maps 16@10x10 F6: layer 84 Full connection Full connection Gaussian conne OUTPUT 10
• Apply filters (or kernels) to the input to produce the output volume • The filter size (or receptive field) is small spatially (3x3 or 5x5) but extends through the full depth of the input volume • Slide each filter across the width and height of the input volume and compute dot products between the filter and the input at any position Convolutional Layer Source: http://cs231n.github.io/convolutional-networks/ 5 filters applied to the input
Convolutional Layer Source: http://cs231n.github.io/convolutional-networks/
• Reduce the spatial size of the representation to limit the number of parameters and avoid overfitting • Downsample the input spatially using MAX operation • Operate independently on every depth slice / feature map of the input (Thus, the depth remains unchanged) Pooling Layer Source: http://cs231n.github.io/convolutional-networks/
Inception V3 Source: Google Codelabs
Don’t be a hero. Transfer learning as a shortcut
Transfer Learning Retraining existing models
Retraining the last layer
Machine Learning on Google Cloud Platform Pre-trained ML models Custom ML models
Google Cloud Vision API
Understand the content of images ● Label Detection ● Optical Character Recognition ● Explicit Content Detection ● Face Detection etc. Google Cloud 
 Vision API Photos: Google Cloud Platform / Kaz Sato @tvirot
Demo https://cloud.google.com/vision/
Custom ML Model with TensorFlow
Demo Based on https://www.tensorflow.org/tutorials/image_retraining
Demo Codelab: https://goo.gl/qi5kiA
Thank you! Ta Virot Chiraphadhanakul, PhD (@tvirot) Google Developer Expert in Machine Learning 
 Managing Director, Skooldio

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