August 2019
Intermediate to advanced
608 pages
7h 46m
Japanese
Content preview from 詳説 Deep Learning ―実務者のためのアプローチ
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付録IDL4Jプロジェクト用のGPUの設定
Vyacheslav Kokorin, Susan Eraly
ニューラルネットワークの学習は、数多くの線形代数演算を伴います。数千のコアを備えるグラフィックスプロセッシングユニット（GPU）は、こうした計算に特化して設計されました。GPUは、学習の高速化、そして長期的にはドルあたり・ワットあたりの計算性能の向上を目的によく使われます。
I.1 バックエンドのGPUへの切り替え
DL4Jは現時点でCUDA 10.0がサポートするNVIDIA GPUを利用できます（CUDA 8.0は対象外になりました）。DL4Jはプラグアンドプレイで記述されています。つまり、計算レイヤーをCPUからGPUに切り替えるときには、pom.mlファイルの依存関係でnd4jの下のartifactIdの行を切り替えるだけ、と簡単です。
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-cuda-10.0-platform</artifactId>
<version>${nd4j.version}</version>
</dependency>
</dependencies>
</dependencyManagement>
I.1.1 GPUの選択
一般論として、ハイエンドコンシューマーグレードのモデルや、プロフェッショナル向けのTeslaデバイスがお勧めです。つまり本書日本語版の翻訳時点（2019年6月）では、1組のNVIDIA GeForce RTX 2070 GPUが最初の選択として手堅いでしょう。
GPUを購入するときに考慮すべき点は以下の通りです。 ...
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