wmhshishen的博客

This notebook is a companion to the book Data Science Solutions (https://www.amazon.com/Data-ScienceSolutions-Startup-Workflow/dp/1520545312). The notebook walks us through a typical workflow for solving data science competitions at sites like Kaggle. There are several excellent notebooks to study data science competition entries. However many will skip some of the explanation on how the solution is developed as these notebooks are developed by experts for experts. The objective of this notebook is to follow a step-by-step workflow, explaining each step and rationale for every decision we take during solution development.

非常好的python机器学习书！ 零、前言 处理，清洗和准备数据。 选择和设计特征。 使用交叉验证选择一个模型并调整超参数。 机器学习的主要挑战，特别是欠拟合和过拟合（偏差和方差权衡）。 对训练数据进行降维以对抗 the curse of dimensionality（维度诅咒） 最常见的学习算法：线性和多项式回归， Logistic 回归，k-最近邻，支持向量机，决策 树，随机森林和集成方法。 第二部分，神经网络和深度学习，包括以下主题： 什么是神经网络？它们有啥优势？ 使用 TensorFlow 构建和训练神经网络。 最重要的神经网络架构：前馈神经网络，卷积网络，递归网络，长期短期记忆网络 （LSTM）和自动编码器。 训练深度神经网络的技巧。 对于大数据集缩放神经网络。 强化学习。 第一部分主要基于 scikit-learn ，而第二部分则使用 TensorFlow 。 注意：不要太急于深入学习到核心知识：深度学习无疑是机器学习中最令人兴奋的领域之 一，但是你应该首先掌握基础知识。而且，大多数问题可以用较简单的技术很好地解决（而 不需要深度学习），比如随机森林和集成方法（我们会在第一部分进行讨论）。如果你拥有 足够的数据，计算能力和耐心，深度学习是最适合复杂的问题的，如图像识别，语音识别或 自然语言处理。

一本很不错的python功能书！ Functional programming defines a computation using expressions and evaluation—often encapsulated in function definitions. It de-emphasizes or avoids the complexity of state change and mutable objects. This tends to create programs that are more succinct and expressive. In this chapter, we'll introduce some of the techniques that characterize functional programming. We'll identify some of the ways to map these features to Python. Finally, we'll also address some ways in which the benefits of functional programming accrue when we use these design patterns to build Python applications.