Neural networks explained with the example of feedforward vs. recurrent networks. Feedforward networks propagate data, while recurrent models allow loops for cascade effects. Recurrent networks are less influential but closer to the brain's function. Introduction to handwritten digit classification

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Sitan Chen from Harvard presents joint work with Yuanzhi Li exploring the provable learnability of a multi-head attention layer in transformers. The talk delves into the architecture of transformers, highlighting the gap between practical success and theoretical understanding. Preliminaries, prior w

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Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

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This study explores the use of K-Nearest Neighbor (KNN) and Multilayer Perceptron (MLP) classifiers for data classification. The KNN algorithm estimates data point membership based on nearest neighbors, while MLP is a feedforward neural network with hidden layers. Parameter tuning and results analys

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Delve into the complexities of control system design, from system identification to modern control techniques. Explore the challenges of designing controls for systems like interferometers and discuss the integration of classical and modern control theories. This workshop offers insights on optimal

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Explore the intricate relationship between feedforward, feedback, and response variability in neural networks. Dive into the impact of noise on effective connectivity and network topology, as well as the challenges posed by noisy and delayed communication between brain regions. Discover the applicat

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