AI Reference 033 Neural Networks & Deep Learning Wikipedia guided Primary sources linked

Convolutional Neural Networks

Definition

Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function.

What is a convolutional neural network?

A convolutional neural network, or CNN, is a neural architecture designed to process grid-like data such as images. Convolution filters slide across an input and detect local patterns using the same learned weights at many positions.

Why convolutions suit images

An edge or texture can appear anywhere in an image. Weight sharing lets one filter detect the same pattern across the image without learning a separate parameter set for every pixel location. Deeper layers can combine simple patterns into more complex shapes.

Historical role

CNN ideas were developed over decades, including work by Yann LeCun on handwritten-digit recognition. Their visibility increased dramatically after AlexNet won the 2012 ImageNet competition, helping trigger the modern deep-learning boom in computer vision.

Related terms, defined

Neural Networks & Deep Learning Recurrent Neural Networks Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function. Neural Networks & Deep Learning Long Short-Term Memory Networks LSTM networks are recurrent neural networks with gated memory mechanisms designed to preserve and control information across longer sequences. Neural Networks & Deep Learning Word Embeddings Word embeddings represent words or tokens as dense numerical vectors whose geometry captures statistical relationships learned from data. Neural Networks & Deep Learning ImageNet and AlexNet AlexNet's strong result in the 2012 ImageNet competition helped demonstrate the effectiveness of deep convolutional neural networks trained on GPUs and large image datasets. Neural Networks & Deep Learning GPUs and the Rise of Deep Learning GPUs perform many numerical operations in parallel, which fits the matrix and tensor workloads used in neural-network training and inference.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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