- Machine Learning With Go
- Daniel Whitenack
- 142字
- 2021-07-08 10:37:24
What you need for this book
To run the examples in this book and experiment with the techniques covered in the book, you will generally need the following:
- Access to a bash-like shell.
- A complete Go environment including Go, an editor, and related default or custom environment variables defined. You can, for example, follow this guide at https://www.goinggo.net/2016/05/installing-go-and-your-workspace.html.
- Various Go dependencies. These can be obtained as they are needed via go get ....
Then, to run the examples related to some of the advanced topics, such as data pipelining and deep learning, you will need a few additional things:
- An installation or deployment of Pachyderm. You can follow these docs to get Pachyderm up and running locally or in the cloud, http://pachyderm.readthedocs.io/en/latest/.
- A working Docker installation (https://www.docker.com/community-edition#/download).
- An installation of TensorFlow. To install TensorFlow locally, you can follow this guide at https://www.tensorflow.org/install/.
推薦閱讀
- Access 數(shù)據(jù)庫應(yīng)用教程
- Mastering Swift 2
- Python面向?qū)ο缶幊蹋簶?gòu)建游戲和GUI
- 編程數(shù)學(xué)
- MongoDB,Express,Angular,and Node.js Fundamentals
- Learning Apache Cassandra
- Training Systems Using Python Statistical Modeling
- Groovy 2 Cookbook
- 啊哈C語言!:邏輯的挑戰(zhàn)(修訂版)
- React and React Native
- Scala編程(第4版)
- Unity虛擬現(xiàn)實(shí)開發(fā)圣典
- TypeScript High Performance
- JavaScript程序設(shè)計基礎(chǔ)教程(慕課版)
- Mastering Citrix? XenDesktop?