## 2 Related Works
### Convolutional Neural Networks  
CNNs are a specialized subclass of artificial neural networks (ANNs) that are particularly well-suited for analyzing visual data. CNNs are designed to automatically and adaptively learn spatial hierarchies of features. This is particularly beneficial for tasks like image recognition, object detection, and even medical image analysis. The concept of residual learning, as introduced by [PERSON] et al., further enhances the capabilities of CNNs by allowing them to benefit from deeper architectures without the risk of overfitting or vanishing gradients [19].  
As opposed to ANNs, CNNs employ local connectivity by linking each neuron to a localized region of the input space. This is in stark contrast to traditional ANNs, where each neuron is connected to all neurons in the preceding and following layers. [PERSON] emphasizes that this local connectivity is crucial for the efficient recognition of localized features in images [20]. Furthermore, they use shared parameters across different regions of the input, which significantly reduces the number of trainable parameters. This is in contrast to traditional ANNs, where each weight is unique, leading to a much larger number of parameters and higher computational costs. CNNs are inherently designed to recognize the same feature regardless of its location in the input space. This is a crucial advantage over traditional ANNs, which lack this form of spatial invariance. Notably, they often employ deeper architectures, which are made computationally feasible through techniques like residual learning, as discussed in [PERSON] et al.'s paper.  
The Inception architecture, introduced by [PERSON] et al., is another example of a deep yet computationally efficient network [21]. CNNs are designed to be computationally efficient, particularly when dealing with high-dimensional data. The architecture leverages local connectivity and parameter sharing to reduce computational requirements. The concept of residual learning, as discussed in the paper by [PERSON] et al., allows CNNs to be trained more efficiently, even when the network is very deep.  
Notably, several unique architectural elements are associated with CNNs, and these include filters, kernels, and pooling layers. Filters and kernels use learnable weight matrices that are crucial for feature extraction. They slide or convolve across the input image to produce feature maps. [PERSON]'s paper highlights the effectiveness of gradient-based learning techniques in training these filters [22]. Pooling layers serve to reduce the spatial dimensions of the input, thereby decreasing computational complexity and increasing the network's tolerance to variations in the input. They are particularly useful in making the network robust to overfitting.  
CNNs can be effectively combined with other types of neural networks, like recurrent neural networks (RNNs), for sequential data processing tasks such as video analysis and natural language processing. Additionally, CNNs can be integrated with traditional machine learning algorithms, like support vector machines (SVMs), for tasks like classification, thereby creating a hybrid model that leverages the strengths of both methodologies. In summary, CNNs offer a robust, adaptable, and computationally efficient approach to a wide range of machine learning tasks. Their unique architecture, as validated by seminal research papers, makes them highly effective for tasks involving spatial hierarchies and structured grid data.