English(EN) Weights for MNIST Handwriting Network [P]

初学者分享 MNIST 分类神经网络权重

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-12 21:35

一位用户分享了他们为分类 MNIST 手写数字而开发的神经网络的权重。该网络包含两个隐藏层，分别有 64 和 32 个神经元，以及一个有 10 个神经元的输出层。该项目是用户首次涉足机器学习，在 MNIST 数据集上达到了 96.52% 的准确率。 AI

影响展示了一个用于学习神经网络实现和评估的基础项目。

排序理由这是个人发布的知名数据集的模型权重，并非来自主要实验室或机构。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 2 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 这是个人发布的知名数据集的模型权重，并非来自主要实验室或机构。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

r/MachineLearning TIER_1 English(EN) · /u/Rollgus · 2026-11-12 21:35

MNIST手写网络权重 [P]

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1xupgn8/weights_for_mnist_handwriting_network_p/"> <img alt="Weights for MNIST Handwriting Network [P]" src="https://preview.redd.it/sfl0qf5b291i1.png?width=140&height=94&auto=webp&s=5d303a539…