This content provides a visual tour of the greatest innovations in Deep Learning and Computer Vision, focusing on Convolutional Neural Networks (CNNs). Before CNNs, neural networks flattened images into a list of pixels, losing spatial information. CNNs, introduced in 1989 by Yann LeCun, preserve the 2D nature of images and process information spatially. The article traces the history of CNNs for Image Classification tasks, highlighting key developments and trends. The heart of a CNN is the convolution operation, which scans filters across images to create feature maps. Multiple convolution layers stacked together form a CNN, which simultaneously performs spatial filtering and combines input channels. The article discusses the 1989 paper by LeCun that introduced training non-linear CNNs using backpropagation. The inductive bias concept in Machine Learning is explained, where specific rules and limitations are introduced to guide model learning towards human-like understanding. CNNs replicate human image classification processes by focusing on spatial filtering and combining patterns for predictions. The design of CNNs allows for parameter-sharing and local pattern recognition, making them less data-hungry compared to feedforward networks. Overall, the article provides insights into the evolution and importance of CNNs in the field of Computer Vision.
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Source link: https://towardsdatascience.com/the-history-of-convolutional-neural-networks-for-image-classification-1989-today-5ea8a5c5fe20?source=rss—-7f60cf5620c9—4
#Evolution of CNNs: Image Classification from 1989 to Present #DeepLearningHistory
