基于豆瓣电影用户数据使用Canopy+K-means聚类的协同过滤推荐 更新对比实验、豆瓣热门电影数据集

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ID3算法构建决策树，并将决策树可视化

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根据数据集中的多个特征进行训练，使得网络模型根据特征可以预测气温。

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Abstract The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model rede- fines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

Titanic数据集主要包含两部分,训练集(train.csv)和测试集(test.csv)。其中训练集中包含乘客的基本信息和最终在事故中的存活情况,测试集只包含乘客的基本信息, 不包含存活情况。 目的:通过对训练集中乘客的基本信息和存活情况的分析,找到背后隐藏的某种规律,去推断测试集中的乘客是否遇难。