1. Convolutional Neural Network
• A child recognize animal visiting a zoo or seeing animal picture.
• Computers ‘see’ in a different way than we do. Their world consists of only numbers.
Every image can be represented as 2-dimensional arrays of numbers, known as pixels.
• But the fact that they perceive images in a different way, doesn’t mean we can’t train
them to recognize patterns, like we do. We just have to think of what an image is in a
different way.

2. Convolutional Neural Network (Cont…)
• Convolutional Neural Network (CNN) specific type of Artificial Neural Network .
• Convolutional Neural Networks are inspired by the brain.
• Research in the 1950s and 1960s by D.H Hubel and T.N Wiesel on the brain of mammals
suggested a new model for how mammals perceive the world visually.

3. Deep Learning Basics
OUTPUTS
HIDDEN
NODES
CAT DOG
• Deep Learning – is a set of machine learning
algorithms based on multi-layer networks
INPUTS

4. Deep Learning Basics
CAT DOG
• Deep Learning – is a set of machine learning algorithms based on
multi-layer networks
4
Training

5. Deep Learning Basics
CAT DOG
• Deep Learning – is a set of machine learning algorithms based on
multi-layer networks
5

6. Deep Learning Basics
CAT DOG
Deep Learning – is a set of machine learning algorithms based on
multi-layer networks

7. Architecture
• Convolutional Neural Networks have a different architecture than regular Neural
Networks. Regular Neural Networks transform an input by putting it through a series of
hidden layers.
• Every layer is made up of a set of neurons, where each layer is fully connected to all
neurons in the layer before. Finally, there is a last fully-connected layer —the output
layer —that represent the predictions.

8. 8
What is Convolutional NN ?
CNN - multi-layer NN architecture
– Convolutional + Non-Linear Layer
– Sub-sampling Layer
– Convolutional +Non-L inear Layer
– Fully connected layers
 Supervised
Feature Extraction
Classi-
fication

9. 9
What is Convolutional NN ?
2x2
Convolution + NL Sub-sampling Convolution + NL

10. Architecture(Cont..)
• The layers are organised in 3 dimensions: width, height and depth.

11. Architecture(Cont..)
CNNs have two components
• The Hidden layers/Feature extraction part:
1. Convolutions
2. Poolingoperations ( which the features are detected)
• The Classification part:
1. Classifier
2. Probability is assigned for the object on the image.

12. Architecture(Cont..)
• Suppose INPUT [32x32x3] ,where width 32, height 32, and with three color channels R,G,B.
• CONV layer may produce [32x32x12] if filters is 12.

13. Architecture(Cont..)
• RELU layer will apply an elementwise activation function, such as the (max(0,x)).
• POOL layer will perform a downsampling operation along the spatial dimensions (width,
height), resulting in volume such as [16x16x12].
• FC (i.e. fully-connected) layer will compute the class scores.

14. Feature Extraction
• Convolution is one of the main building blocks of a CNN.
• At every location, a matrix multiplication is performed and sums the result onto the
feature map.
• The area of our filter is also called the receptive field, named after the neuron cells! The
size of this filter is 3x3.

15. Feature Extraction(Cont…)
Need for receptive field
• For example, suppose that the input volume has size [32*32*3].So
our connection would be 32*32*3 ,which is impractical. If the
receptive field (or the filter size) is 5*5, then each neuron in the Conv
Layer will have weights to a [5x5x3] region in the input volume, for a
total of 5*5*3 = 75 weights (and +1 bias parameter). Notice that the
extent of the connectivity along the depth axis must be 3, since this is
the depth of the input volume.

16. Feature Extraction(Cont…)
• Just like any other Neural Network, we use an activation function to make our output non-
linear. This could be the ReLU activation function.
• Stride is the size of the step the convolution filter moves each time. A stride size is usually 1,
meaning the filter slides pixel by pixel.
• This shows stride size 1 in action.

17. Feature Extraction(Cont…)
• Because the size of the feature map is always smaller than the input, we have to do
something to prevent our feature map from shrinking. This is where we use padding.
• Pooling layer reduce the dimensionality of parameters and computation in the network.
• The most frequent type of pooling is max pooling, which takes the maximum value in each
window.

18. Feature Extraction(Cont…)
• Convolutional Demo

19. Classification
• Classification part consists fully connected layers.
• It accept 1 Dimensional data.
• To convert our 3D data to 1D, function flatten in Python.

20. CNN applications
CNN is a big hammer Plenty low hanging fruits
You need just a right nail!

21. Conv NN: Detection

22. Conv NN: Scene parsing

23. CNN: indoor semantic labeling RGBD

24. Conv NN: Action Detection

25. Conv NN: Image Processing

26. Summary
• CNNs are especially useful for image classification and recognition.
• The main technique in CNNs is convolution.
• A filter slides over the input and merges the input value + the filter value on the feature
map.
• Feed new images to our CNN so it can give a probability for the object it thinks it sees
or describe an image with text.