2 个仓库
Awesome GitHub RepositoriesVisual Element Customization
Configuration of plot components like axes, legends, and annotations for improved readability.
Distinct from Plot Element Scaling: Distinct from scaling: focuses on aesthetic and informational configuration of elements rather than just size adjustments.
Explore 2 awesome GitHub repositories matching data & databases · Visual Element Customization. Refine with filters or upvote what's useful.
Awesome Visual Element Customization GitHub Repositories
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- morvanzhou/tutorials
MorvanZhou/tutorials
12,952在 GitHub 上查看This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad
Provides instructions on configuring axes, legends, and annotations to improve visual data readability.
Pythonmachine-learningmultiprocessingneural-network - mrdbourke/zero-to-mastery-ml
mrdbourke/zero-to-mastery-ml
5,839在 GitHub 上查看本项目是一个机器学习教育课程和学习平台，通过交互式 Jupyter Notebooks 提供。它作为掌握 Python 数据科学工具包的综合指南，为数值计算、表格数据操作和统计可视化提供结构化教程。 该课程包括 Scikit-Learn 的具体实现指南，以及关于构建、训练和部署神经网络及计算机视觉模型的 TensorFlow 实践课程。它涵盖了构建预测模型的端到端过程，从初始问题定义和任务分类，到通过交互式 Web 界面部署模型。 该项目涵盖了广泛的功能领域，包括多维数组的数值计算、探索性数据分析和数据预处理例程。它为监督和无监督学习、自动化机器学习流水线、超参数优化以及使用分类指标和交叉验证的模型评估提供了详细的工作流。 教育内容组织为一系列 Notebook，将 Python 代码与叙述性解释交织在一起，以记录数据科学工作流。
Configures plot components such as axis limits, color schemes, and legends to improve clarity.
Jupyter Notebookdata-sciencedeep-learningmachine-learning
