Introduction to Convolutional Neural Networks (CNN)
A convolutional neural network (CNN) is a leading deep learning model predominantly employed for image processing and other computer vision tasks. It's designed to identify and extract essential information from images and then employ this data for various prediction or classification tasks.
Basic Working Principle of CNN
- Convolutional Layers: At the heart of a CNN, convolutional layers facilitate the extraction of vital image components such as edges, textures, and segments. This extraction is achieved via the convolution operation that uses learnable filters (often termed as kernels or weights) on the input image. As these filters traverse the image, they create a feature map by computing the dot product between the filter and the associated segment of the image.
- Pooling Layers: Post convolution, pooling layers come into play. They deploy a max operation to reduce the spatial scale of the produced feature maps, making the CNN less reactive to minor input image alterations.
- Fully Connected Layers: After gleaning features from the image, these features are routed through one or more fully connected layers, culminating in the final prediction or categorization.
CNN Architecture Variations
While the basic structure of a CNN comprises layers like input, convolutional, pooling, normalization, fully connected, and output layers with accompanying regularization techniques to mitigate overfitting, there are advanced architectural variants:
- Sequence Modelling Architectures: Such as RNN and LSTM.
- Residual Connections: E.g., ResNet.
- Attention Mechanisms: Notably, the Transformer.
- Task-Specific Architectures: Examples include YOLO or Faster R-CNN for object detection and BERT for language processing.
Parameter Calculation in CNN
The size of a CNN's parameters hinges on factors like the number of filters in each convolutional layer, the kernel size (dictating each filter's size), the employed stride in each layer, and neuron count in the densest layers. While standard equations can enumerate parameters for each layer, the aggregate CNN parameters are the sum of individual layer parameters. Tools like Python's Pytorch and TensorFlow greatly streamline the task of computing a model's parameter tally.