Convolutional Neural Networks: A Quick Overview Artificial neural networks (ANNs) are the next big thing in AI. Their prominence has led to hundreds of white papers that help portray the latest research & development in the field. ANNs are incredibly powerful and are the closest to what we have to an artificial replica of the human brain that can mimic complex cognitive processes. This article dwells on one of the most potent variants of ANNs, Convolutional Neural Networks. What Is A Convolutional Neural Network? Convolutional neural networks are similar to generic neural networks used extensively in pattern recognition and computer vision. Like ANNs, they consist of neurons with learnable weights and biases. The primary difference between a CNN and any other ANN is that CNNs assume inputs to be images; thus, their properties & architecture, thus, are designed accordingly. The architecture of CNNs has their neuron layers arranged in three specific dimensions, namely, width, height, and depth, and all are activation functions. Neurons in a CNN are only connected to a particular region of the preceding layer instead of every neuron in a fully-connected manner. Like any generic neural network, the hidden layers carry heavy lifting. Understanding how a convolution neural network works and designing & implementing your own is a tad challenging. Study extensively and supplement your learning with professional assistance from prominent online paper help services.
ANNs are incredibly powerful and are the closest to what we have to an artificial replica of the human brain that can mimic complex cognitive processes.
