CONVOLUTIONAL NEURAL NETWORKS Convolutional neural networks (CNN) are a type of deep learning architecture designed to extract features from image inputs. CNNs are commonly used in computer vision and image processing applications. CNNs have a three-layer architecture ( Figure 1 ). Convolutional layer — feature extraction Pooling layer — dimension reduction Feedforward network — output classification Figure 1. Basic CNN structure architecture. Features from an input (e.g., an

ARTIFICIAL NEURAL NETWORKS Artificial neural networks , also known as deep learning networks, are a type of machine learning algorithm, which can extract exceedingly complex features from input data. Examples of input data features are the words in a sentence or the patterns in an image. In analogy to the huma n brain’s cognitive processes, deep learning networks acquire the ability to identify feature patterns ( learn )  by modulating the activation state of the artificial n

I provide in this post a high-level introduction to artificial neural networks in the context of MS-proteomics.