hands-machine-learning-scikit-learn-tensorflow下载

weixin_39822095 2019-09-28 10:00:27

Scikit-Learn与Tensorflow机器学习实用指南的英文影印版，高清可搜索，有书签。
相关下载链接：//download.csdn.net/download/cloud_sp/10204424?utm_source=bbsseo

...全文

39 回复打赏收藏转发到动态举报

写回复

回复

切换为时间正序

请发表友善的回复…

发表回复

Hands-On Machine Learning with Scikit-Learn and TensorFlow [PDF] [HTD 2017]Hands-On Machine Learning with Scikit-Learn and TensorFlow [PDF] [HTD 2017]Hands-On Machine Learning with Scikit-Learn and TensorFlow [PDF] [HTD 2017]

When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search. Where does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia, has my computer really “learned” something? Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. It will be a high-level overview (the only chapter without much code), all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book. So grab a coffee and let’s get started!

原版PDF Hands-On Machine Learning with Scikit-Learn and TensorFlow 不是早期预览版（EAP），而是正式版

讲述scikit-learn和TensorFlow, 高清版，良心数据，英文版1st

Oreilly 2017最新五颗星 Deep Learning + Tensorflow + Scikit-Learn 书籍。

下载资源悬赏专区

13,654

社区成员

12,578,716

社区内容

发帖

与我相关

我的任务

其他技术论坛（原bbs）

社区管理员

加入社区

近7日
近30日
至今

加载中

查看更多榜单

社区公告

暂无公告

试试用AI创作助手写篇文章吧

+ 用AI写文章