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DL4JのNeural Netはオンラインでの学習、データパイプライン、Real Time Streamingをサポート。Apache Kafka, spark-streamingなど。
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分散環境での実行について。Trainを直列実行するより、並列実行するほうが効率的。 Hardware Accelerationを使う理由。Mobileの電池の消費を抑えたり、C++による計算の効率化と、分散環境でのJVM上での実行を両立するため。
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Hardware Acceleration OpenCL ... クロスプラットフォーム Cuda ... NVIDA MKL .. Intel Normal Java ... 色んな場所で実行可（Androidなど)
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Deep Learning4j はHadoop, Spark, Mesosなどの分散処理のRuntimeに対応している。またGPUかCPUか、計算するcontextを自由に選択することもできる。
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Canovaがあらゆるデータを合成可能な形式に変換する。vectorizationの「ロゼッタストーン」
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Japanese largest Scala conference will be held: 2016/1/30-31https://twitter.com/OE_uia/status/613302216443596800 …
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Night at Museum with Dinasaurs and Tokyo Tower http://twitpic.com/2rk7pg
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Moon cake fest at Takashimaya Orchard http://twitpic.com/2r2jrt
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