CNN - Applications

Convenlutional Neural Networks

Overview
Alternative View
More detail about CNN

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Ignoring units (i.e. neurons) with probability $1-p$ during the training phase of certain set of neurons which is chosen at random
These units are not considered during a particular forward or backward pass

Learn more robust features that are useful in conjunction with many different random subsets of the other neurons.
Roughly doubles the number of iterations required to converge. However, training time for each epoch is less

Data Argument

Early Stopping

Well Known ConvNets

LeNet
AlexNet
ZF Net
GoogleNet
VGGNet
ResNet

Application

* More detail about CNN *

$G(m,n)=(f*h)[m,n]=\sum_{j}\sum_k h(j,k)f[m-j,n-k]$

Padding: $p = (f-1)/2$

Striding

$n_{out}=\lfloor\frac{n_{in}+2p-f}{s}+1\rfloor$

The third dimension

Tensors Dimensions

Connections Cutting and Parameters Sharing

Two important features of CNN

Connections Cutting
Parameters Sharing

Convolutional Layer Backpropagation

$dZ^{[l]}=dA^{[l]}*g^{'}(Z^{[l]})$

$dA+=\sum_{m=0}^{n_h}\sum_{n=0}^{n_w}W\cdot dZ[m,n]$

Pooling Layer

Pooling Layers Backpropagation

Machine Learning Applications and practices