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!!!