- Machine Learning With Go
- Daniel Whitenack
- 159字
- 2021-07-08 10:37:32
Statistics
At the end of the day, the success of your machine learning application is going to come down to the quality of your data, your understanding of the data, and your evaluation/validation of the results. All three of these things require us to have an understanding of statistics.
The field of statistics helps us to gain an understanding of our data, and to quantify what our data and results look like. It also provides us with mechanisms to measure how well our application is performing and prevent certain machine learning pitfalls (such as overfitting).
As with linear algebra, we aren't able to give a complete introduction to statistics here, but there are many resources online and in print to learn introductory statistics. Here we will focus on a fundamental understanding of the basics, along with the practicalities of implementation in Go. We will introduce the idea of distributions, along with an introduction to quantifying and visualizing these distributions.
- Java Web基礎與實例教程(第2版·微課版)
- PostgreSQL Cookbook
- 深入淺出Spring Boot 2.x
- aelf區塊鏈應用架構指南
- Mastering Ubuntu Server
- 游戲程序設計教程
- Magento 1.8 Development Cookbook
- FFmpeg入門詳解:音視頻原理及應用
- PhpStorm Cookbook
- Java 9模塊化開發:核心原則與實踐
- D3.js 4.x Data Visualization(Third Edition)
- Android底層接口與驅動開發技術詳解
- Python忍者秘籍
- Kotlin編程實戰:創建優雅、富于表現力和高性能的JVM與Android應用程序
- ServiceNow:Building Powerful Workflows