Understanding CNN Operations in Deep Learning

Dec 31, 2024

Deep Learning Tutorial 23: CNN Operations

Introduction

  • Presenter: Krishna
  • Welcome to the 23rd tutorial on the deep learning playlist.
  • Apologized for the delay in uploading the tutorial due to setup issues.
  • Committed to uploading regularly and completing the series in two weeks.

Tutorial Focus

  • Discuss the operations of Convolutional Neural Networks (CNNs).
  • Compare CNN operations with Artificial Neural Network (ANN) operations.

Artificial Neural Network (ANN) Operations

  • Inputs: Denoted as X1, X2, X3.
  • Weights: Assigned as W1, W2, W3.
  • Process:
    • Multiply inputs with corresponding weights.
    • Add bias.
    • Apply activation functions (ReLU, Sigmoid).

Convolutional Neural Network (CNN) Operations

  • Input Image: Any size, example given 4x4.
  • Filters (or Kernels):
    • Used for edge detection, etc.
    • Example filter size given 2x2, results in a 3x3 output if no padding or strides are used.
  • Convolution Process:
    • Multiply each pixel with the corresponding filter value.
    • Sum the results.
  • Activation Function: Apply ReLU function to output.
  • Backpropagation:
    • Update filter values.
    • Calculate loss, backpropagate, update weights.

Key Differences Between ANN and CNN

  • ANN multiplies weights with inputs and applies an activation function post multiplication.
  • CNN applies filters, performs convolution, and applies an activation function on each field.
  • CNN can stack multiple layers horizontally for deeper learning similar to brain regions responsible for vision.

Stacking Convolution Layers

  • Horizontal stacking for complex feature detection (e.g., detecting different parts of a cat's face).
  • Initial filter values are random and updated through backpropagation.

Max Pooling (Covered in Next Video)

  • Explains location invariance in CNNs.
  • Helps CNN automatically trigger neurons for feature detection.

Conclusion

  • Summary of CNN vs ANN operations.
  • Mention of related content on transitioning to data science.
  • Recommendation to visit Springboard India for discussions with data scientists.
  • Encouragement to subscribe for more content.

Note: Next video will cover max pooling in detail and its significance in CNN operations.