[IT研修]注目キーワード Python UiPath(RPA) 最新技術動向 Microsoft Azure Docker Kubernetes 第49回　大規模レコメンデーションシステムのアーキテクチャー（パート2） (中井悦司) 2018年11月 はじめに 前回に引き続き、2016年に公開された論文「Deep Neural Networks for YouTube Recommendations」をもとにして、ビデオストリーミングサービス（YouTube）のレコメンデーションシステムを支える機械学習のアーキテクチャーを紹介します。前回の図2に示したように、このシステムでは、「候補選択モデル」と「ランキングモデル」を組み合わせた仕組みが用いられています。今回は、候補選択モデルの部分について、その仕組みを少し掘り下げて解説したいと思います。 協調フィルタリングの復習 前回の最後に少し触れ

Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters. The exponential family has recently been extended to the t-exponential family, which contains Student-t distributions as family members and thus allows us to handle noisy data well. However, since the t-exponential family is defined by the deformed

Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured by

Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured by