1. どんなもの？

Showcasing the versatility of the `{kindling}` R package.

In this paper, we advance the understanding of neural network training dynamics by examining the intricate interplay of various factors introduced by weight parameters in the initialization process. Motivated by the foundational work of Luo et al. (J. Mach. Learn. Res., Vol. 22, Iss. 1, No. 71, pp 3327-3373), we explore the gradient descent dynamics of neural networks through the lens of macroscopic limits, where we analyze its behavior as width $m$ tends to infinity. Our study presents a unified approach w

Dive into CNN, the backbone of Computer Vision, understand its mathematics, implement it from scratch, and explore its applications

当前很多人体行为识别分类器都是基于从原始图像上手工提取的特征，本文提出的3D CNN能够直接从原始输入中提取特征，通过执行3D卷积在监控视频中从时间和空间维度提取特征，将高级功能模型规范化，并结合各种不同模型的输出，进一步提高3D CNN的性能。在机场的监控视频中，该方法相比于传统的方法，取的了卓越的性能。

In this blog post I will show how to use neural networks in Ruby to solve a simple classification problem

Abstract page for arXiv paper 2305.17346: Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing

Discover what part of the brain controls visualization. Explore the neural networks behind mental imagery, from the visual cortex to aphantasia

In the previous article, we covered the learning process of ANNs using gradient descent. However, in the last few sentences, I've mentioned that some rocks were left unturned. Specifically, explanation of the backpropagation algorithm was skipped. Also, I've mentioned it is a somewhat complicated algorithm and that it deserves the whole separate blog post. So…

Normalization in Neural Networks and the Human Brain: Understand the importance of data normalization in machine learning and its connection to human cognition