- Deep Learning with R for Beginners
- Mark Hodnett Joshua F. Wiley Yuxi (Hayden) Liu Pablo Maldonado
- 255字
- 2021-06-24 14:30:38
MXNet
MXNet is a deep learning library developed by Amazon. It can run on CPUs and GPUs. For this chapter, running on CPUs will suffice.
Apache MXNet is a flexible and scalable deep learning framework that supports convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). It can be distributed across multiple processors/machines and achieves almost linear scale on multiple GPUs/CPUs. It is easy to install on R and it supports a good range of deep learning functionality for R. It is an excellent choice for writing our first deep learning model for image-classification.
MXNet originated at Carnegie Mellon University and is heavily supported by Amazon; they chose it as their default deep learning library in 2016. In 2017, MXNet was accepted as the Apache Incubator project, ensuring that it would remain open source software. It has a higher-level programming model similar to Keras, but the reported performance is better. MXNet is very scalable as additional GPUs are added.
To install the MXNet package for Windows, run the following code from an R session:
cran <- getOption("repos")
cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN"
options(repos = cran)
install.packages("mxnet")
This installs the CPU version; for the GPU version, you need to change the second line to:
cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
You have to change cu92 to cu80, cu90 or cu91 based on the version of CUDA installed on your machine. For other operating systems (and in case the this does not work, as things change very fast in deep learning), you can get further instructions at https://mxnet.incubator.apache.org/install/index.html.
- Developing Mobile Games with Moai SDK
- 正則表達式必知必會
- 大數據:規劃、實施、運維
- Neural Network Programming with TensorFlow
- 一個64位操作系統的設計與實現
- Spark大數據分析實戰
- 區域云計算和大數據產業發展:浙江樣板
- 大數據數學基礎(Python語言描述)
- 數據庫查詢優化器的藝術:原理解析與SQL性能優化
- Unity Game Development Blueprints
- 數據挖掘算法實踐與案例詳解
- C# 7 and .NET Core 2.0 High Performance
- Nagios Core Administrators Cookbook
- SQL應用開發參考手冊
- Working with OpenERP