Python, neural network, MNIST

Recurrent Neural Network(RNN) is a type of Neural Network where the output from previous step are fed as input to the current step

Rectifier linear unit or its more widely known name as ReLU becomes popular for the past several years since its performance and speed. In contrast to other common activation functions, ReLU is a linear function. In other words, its derivative is either 0 or 1.

# Recurrent Neural Network (RNN) Recurrent Neural Networks (RNNs) are a type of artificial neural network that has a chain-like structure especially well-suited to operate on sequences and lists. RNNs are applied to a wide variety of problems where text, audio, video, and time series data is present. This may include speech recognition, detection of stock trading patterns, analysis of DNA sequences, language modeling, translation, image captioning, and more. ### How RNNs Differ from Vanilla Feed-forward N

Abstract page for arXiv paper 2410.06074: Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning

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This paper investigates how structured interregional connectivity and chaotic local dynamics interact to selectively route neural signals, balancing complexity and stability

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Evaluation of FPGA Acceleration of Neural Networks 21 pages•Published: December 11, 2023 Abstract This paper explores real-time Convolutional Neural Network inference on Field Pro- grammable Gate Arrays (FPGAs) implemented in Synchronous Message Exchange (SME). We compare SME to the widespread FPGA tool, High-Level Synthesis (HLS), and com- pare both the SME and HLS implementations of CNNs with the PyTorch implementation for CNN on CPU/GPU. We find that the SME implementation is more flexible than the HLS

This talk is about computing with discrete compositional data structures in analog computers. A core issue for both computer science and cognitive neuroscience is the degree of match between a class of computer designs and a class of computations. In cognitive science, it is manifested in the apparent mismatch between the neural network hardware of the brain (essentially, a massively parallel analog computer) and the computational requirements of higher cognition (statistical constraint processing with comp