Convolutional Networks in the High-Dimensional Data Maze
Learn about convolutional networks and their role in processing high-dimensional data like images and multivariate time series. Understand convolutional constructs, pooling techniques, and their impact on network efficiency and accuracy. This lesson guides you through the foundational principles and practical applications of CNNs in handling complex data structures.
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The quote by Sherlock Holmes, “We are suffering from a plethora of surmise, conjecture, and hypothesis. The difficulty is to detach the framework of fact—of absolute undeniable fact—from the embellishments,…” serves as an apt introduction to the challenges faced in navigating the intricate landscape of high-dimensional data. Just as Holmes seeks to discern undeniable facts from speculative embellishments, so too do data scientists grapple with the task of distilling essential truths from the overwhelming complexity of information.
High-dimensional inputs are common. For example, a) images are high-dimensional in space, b) multivariate time series are high-dimensional in both space and time. Several deep learning (or machine learning) models get drowned in the excess confounding information when modeling such data, except for “convolutional networks.” They specialize in addressing this ...