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Deep learning has transformed many fields by enabling machines to automatically learn data representations. In network analysis, deep learning plays a pivotal role in representation learning, facilitating the extraction of meaningful features from graphs for tasks such as classification, clustering, and link prediction.
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Ordinary Differential Equations (ODEs) play a critical role in deep learning, providing a mathematical framework for modeling continuous transformations in neural networks. Below, we explore seven key examples where ODEs intersect with deep learning, accompanied by real-world applications and references.
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A network is a collection of objects, known as nodes (or vertices), connected by relationships called edges (or links). The study of networks, also referred to as graph theory, has applications in multiple disciplines, including sociology, biology, computer science, and economics.
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In this post, we explore the a practical example of simulating a coin toss experiment with a Linear Congruential Generator (LCG), a well-known random number generator (RNG). We’ll illustrate how the probabilities of heads and tails converge to 0.5 as the number of tosses increases, validating the frequency approach to probability.