Convolutional Neural Networks

References:

1. https://www.ibm.com/topics/convolutional-neural-networks
2. https://d2l.ai/chapter_natural-language-processing-applications/sentiment-analysis-cnn.html
3. https://developer.ibm.com/articles/introduction-to-convolutional-neural-networks/

What are CNNs?

• Three main layers: Convolutional, Pooling and Fully-Connected Layer
• Convolutional Layer:
  • Core building block
  • Requires input data, filter and feature map.
• Pooling layer – downsampling, dimensionality reduction
• FC Layer – peforms task
• Types – AlexNet, VGGNet, GoogleNet, ResNet, LeNet-5 (classic CNN architecture)

Different steps:

1. Image channels
   • Way to represent image in numerical format.
   • Pixel represents image. Each pixel mapped to number between 0 to 255 (representing color ranging from 0 to 255)
   • Image represented in 3D array each for Red, Green and Blue values
2. Convolution
   • Extract key features from image
   • Filter or kernel used
   • Filter strided over input array to produce output array called feature map.
3. Pooling
   • Adding extra layers of zeros to outer rows and columns in the input array.
4. Flattening
   • make outputs compatible with an artificial neural network
   • multidimensional array to A matrix with one column and n rows (nx1) also called a column vector.
5. Full Connection
   • typically towards the end of neural network architecture
   • also known as dense layers