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Deep Learning A-Z™: Convolutional Neural Networks (CNN) - What Are Convolutional Neural Networks
The document discusses deep learning and convolutional neural networks. It includes diagrams of CNNs that take input images and output labels. CNNs use steps of convolution, max pooling, flattening and full connection. The document also references Yann LeCun's 1998 paper on applying gradient-based learning to document recognition and includes a link to the paper.
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Deep Learning A-Z™: Convolutional Neural Networks (CNN) - What Are Convolutional Neural Networks
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- 6. © SuperDataScienceDeep LearningA-Z Image Source: a talk by Geoffrey Hinton
- 7. © SuperDataScienceDeep LearningA-Z Source: google trends
- 8. © SuperDataScienceDeep LearningA-Z Yann Lecun
- 9. © SuperDataScienceDeep LearningA-Z Google Facebook
- 10. © SuperDataScienceDeep LearningA-Z Input Image CNN Output Label (Image class)
- 11. © SuperDataScienceDeep LearningA-Z CNN Happy CNN Sad
- 12. © SuperDataScienceDeep LearningA-Z Pixel 1 Pixel 2 Pixel 3 Pixel 4 2d array B / W Image 2x2px Pixel 1 Pixel 2 Pixel 3 Pixel 4 Colored Image Colored Image Pixel 1 Pixel 2 Pixel 3 Pixel 4 Red channel Green channel Blue channel 3d array Colored Image 2x2px Pixel 1 Pixel 2 Pixel 3 Pixel 4
- 13. © SuperDataScienceDeep LearningA-Z 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0
- 14. © SuperDataScienceDeep LearningA-Z STEP 1: Convolution STEP 2: Max Pooling STEP 3: Flattening STEP 4: Full Connection
- 15. © SuperDataScienceDeep LearningA-Z Gradient-Based Learning Applied to Document Recognition By Yann LeCun et al. (1998) Link: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf Additional Reading: