机器学习研究和开发所需的组件列表

• 线性代数： 机器学习开发人员需要数据结构，如向量，矩阵和张量，它们具有紧凑的语法和硬件加速操作。其他语言的例子：NumPy，MATLAB和R标准库，Torch。
• 概率论： 各种随机数据生成：随机数和它们的集合; 概率分布; 排列; 收集，加权抽样等等。示例：NumPy和R标准库。
• 数据输入输出： 在机器学习中，我们通常最感兴趣的是以下列格式解析和保存数据：纯文本，CSV等表格文件，SQL等数据库，Internet格式JSON，XML，HTML和Web抓取。还有很多特定于域的格式。
• 数据争用： 类似表的数据结构，数据工程工具：数据集清理，查询，拆分，合并，改组等。Pandas，dplyr。
• 数据分析/统计： 描述性统计，假设检验和各种统计资料。R标准库，以及很多CRAN包。
• 可视化： 统计数据可视化（非饼图）：图形可视化，直方图，马赛克图，热图，树状图，3D表面，空间和多维数据可视化，交互式可视化，Matplotlib，Seaborn，Bokeh，ggplot2，ggmap，Graphviz，D3 .js。
• 符号计算： 自动区分：SymPy，Theano，Autograd。
• 机器学习包： 机器学习算法和求解器。Scikit-learn，Keras，XGBoost，E1071和caret。
• 交互式原型设计环境： Jupyter，R studio，MATLAB和iTorch。

Here is a list of components that are needed for the successful machine learning research and development, and examples of popular libraries and tools of the type:

• Linear algebra: Machine learning developer needs data structures like vectors, matrices, and tensors with compact syntax and hardware-accelerated operations on them. Examples in other languages: NumPy, MATLAB, and R standard libraries, Torch.
• Probability theory: All kinds of random data generation: random numbers and collections of them; probability distributions; permutations; shuffling of collections, weighted sampling, and so on. Examples: NumPy, and R standard library.
• Data input-output: In machine learning, we are usually most interested in the parsing and saving data in the following formats: plain text, tabular files like CSV, databases like SQL, internet formats JSON, XML, HTML, and web scraping. There are also a lot of domain-specific formats.
• Data wrangling: Table-like data structures, data engineering tools: dataset cleaning, querying, splitting, merging, shuffling, and so on. Pandas, dplyr.
• Data analysis/statistic: Descriptive statistic, hypotheses testing and all kinds of statistical stuff. R standard library, and a lot of CRAN packages.
• Visualization: Statistical data visualization (not pie charts): graph visualization, histograms, mosaic plots, heat maps, dendrograms, 3D-surfaces, spatial and multidimensional data visualization, interactive visualization, Matplotlib, Seaborn, Bokeh, ggplot2, ggmap, Graphviz, D3.js.
• Symbolic computations: Automatic differentiation: SymPy, Theano, Autograd.
• Machine learning packages: Machine learning algorithms and solvers. Scikit-learn, Keras, XGBoost, E1071, and caret.
• Interactive prototyping environment: Jupyter, R studio, MATLAB, and iTorch.

摘录来自: Oleksandr Sosnovshchenko. “Machine Learning with Swift: Artificial Intelligence for iOS。” Apple Books.