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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Image Source: a talk by Geoffrey Hinton

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Source: google trends

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Yann Lecun

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Google Facebook

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Input Image CNN
Output
Label
(Image
class)

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CNN Happy
CNN Sad

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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

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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

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STEP 1: Convolution
STEP 2: Max Pooling
STEP 3: Flattening
STEP 4: Full Connection

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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: