Convolutional Neural Networks

A convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex. Individual cortical neurons respond to stimuli in a restricted region of space known as the receptive field. The receptive fields of different neurons partially overlap such that they tile the visual field.

Statistical Invariance — Things that don’t change over time or space.

‘Same’ padding equation:
 out_height = ceil(float(in_height)/ float(strides[1]))
 out_width = ceil(float(in_width)/ float(strides[2]))

‘Valid’ padding equation:
 out_height = ceil(float(in_height — filter — height + 1)/ float(strides[1]))
 out_weight = ceil(float(in_width — filter — width + 1)/ float(strides[2]))

Advanced ConvNet-ology


  1. Max Pooling:
     — Parameter — free!
     — Often more accurate
     — More expensive
     — More hyperparamters — pooling size and pooling strides
  2. Average Pooing:
     — Y = mean(Xi)

1 x 1 convolutions: 1 x 1 convolutions ⇔ Matrix multipliers

Inception modules:

At each layer of your convNet yu make a choice. Have a pooling operation or convolution or 1 x 1, and on on top concatenate the output of each other. We choose in such a way so as to decrease the parameters so that the model performs better than a simple convolution.

Alright that’s it for now! Thank you for spending your time. Cheers!

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