"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

February 07, 2017

Day #54 - Fundamental Concepts - Artificial Neural Networks

Referenced Articles - Link

One liner definitions
  • Image - Represented as RGB Matrix with Height and width = 3 color channels X Height X width
  • Color represented in [0,255] Range
  • Kernel - Small Sized matrix consists of real-valued entries
  • Activation Region - Region where features specific to kernel detected in input
  • Convolution - Calculated by taking dot product of corresponding values of kernel and input matrix certain selected coordinates
  • Zero Padding - Systematically adding inputs to adjust size based on requirements
  • Hyperparameter- Properties pertaining to the structure of layers and neurons (spatial arrangement, receptive field values called hyperparameters). Main CNN hyperparameters are R - Receptive Field, Zero Padding - P, input volume dimension ( Width X Height X Depth) and Stride Length (S)
  • Convolutional Layer - Convolution operation with input filters and identifying the activation region. Convolutiuon Layer output - ReLu (Activation Values)
  • ReLu - Rectified Linear Unit Layer. Most commonly deployed activation function for output of CNN neurons. max(0,x)
  • ReLu is not differentiable with origin so we use Softplus function ln(1+e^x). Derivative of Softplus function is sigmoid function
  • Pooling - Placed after convolution. Objective is downsampling (reduce dimensions)
  • Advantages of downsampling
    • Decreased size of input for upcoming layers
    • Works against overfitting
  • Pooling takes sliding window across input transforming into representative values. Transformation performed by taking maximum value in observable window (max pooling)
Happy Learning!!!

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