CNN Learning One pagers

Product and Example

• https://tangoeye.ai/
• Retail solutions built on
• Age Detection Models
• Gender Detection Models
• Face Detection
• Re-identification

Models for Training - tensorflow hub

How CNN works - Visualizer

How Features are Learned - 10 class classification

But what is a neural network

The Complete Mathematics of Neural Networks and Deep Learning

Learning Procedure

• Step 1: Take a batch of training data and perform forward propagation to compute the loss.
• Step 2: Backpropagate the loss to get the gradient of the loss with respect to each weight.
• Step 3: Use the gradients to update the weights of the network.

Backprop summary

• Chain rule derivate
• The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between actual output and desired output
• Ability to create new distinguishing features
• The aim is to find the set of weights that ensure that for each input vector the output vector produced by the network is same as the desired output vector
• The drawback in learning procedure is that the error surface may contain local minima so that gradient descent is not guaranteed to find a global minimum

Activation Functions

• Introduce non-linearity into a model
• We need non-linearity, to capture more complex features and model more complex variations that simple linear models can not capture.
• neural networks use non-linear activation functions, which can help the network learn complex data, compute and learn
• Signmoid, Tanh, Relu

Designing CNN

• The first rule of thumb is that you should not try to design your own architecture from scratch
• If you are working on generic problem, it never hurts to start with ResNet-50. If you are building a mobile-based visual application where there is limited computation resources, try MobileNets

Keep Learning!!!