slick-doc-ja 3.0 — Combined Pages
slick-doc-ja 3.0 Slick 3.0 documentationã®æ—¥æœ¬èªžè¨³ã§ã™ã€‚ 編集先: GitHub - krrrr38/slick-doc-ja 連絡先: @krrrr38 ä»–ã®ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã®ãƒ‰ã‚ュメント Slick 1.0 翻訳 Slick 2.0 翻訳 Slick 3.0 翻訳 API Documentation (scaladoc) Slick Core (slick) TestKit (slick-testkit) Code Generator (slick-codegen) Direct Embedding (slick-direct) (Deprecated) Slick Extensions (slick-extensions) Slick 3.0.0 documentation - 01 Introduction Permalink to Introdu
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ã“ã‚Œã¯ã€ã€ŒBASE Advent Calendar 2018ã€ã®6日目ã®è¨˜äº‹ã§ã™ã€‚ DataStrategyã®é½‹è—¤(@pigooosuke)ãŒæ‹…当ã—ã¾ã™ã€‚ devblog.thebase.in ã¯ã˜ã‚㫠機械å¦ç¿’エンジニアã®äººã¯ã€åˆ†é¡žã‚„回帰ãªã©ã®èª²é¡Œã«å–り組むã«ã‚ãŸã£ã¦ã€å‰ã„人や導入先ã®éƒ¨é–€ã‹ã‚‰ã€Œãã®äºˆæ¸¬ã©ã‚Œãらã„外れるã®ï¼Ÿã€ã€Œå¦ç¿’モデルã®äºˆæ¸¬ã«å¯¾ã—ã¦ã©ã†ãƒªã‚¹ã‚¯è©•ä¾¡ã‚’ã™ã‚Œã°ã„ã„ã®ï¼Ÿã€ã¨å°‹ãられるã“ã¨ã¯ã‚ã‚Šã¾ã›ã‚“ã‹ï¼Ÿ ãã®ã‚ˆã†ãªå ´é¢ã§æ´»èºã™ã‚‹ã‹ã‚‚ã—ã‚Œãªã„Quantile Regression(分ä½ç‚¹å›žå¸°)ã®ãŠè©±ã‚’ã—ã¾ã™ã€‚ 回帰モデルã®è©•ä¾¡ カテゴリーを予測ã™ã‚‹ã‚ˆã†ãªåˆ†é¡žå•é¡Œã§ã¯ã€å„クラスã§ã®ç²¾åº¦ã‚’確èªã™ã‚‹ã“ã¨ã¯ã§ãã¾ã™ã€‚ã—ã‹ã—ã€å£²ä¸Šã‚„何ã‹ã—らã®å€¤ã‚’予測ã™ã‚‹å›žå¸°å•é¡Œã§ã¯ã€ãã®ãƒ¢ãƒ‡ãƒ«ã«ãŠã‘る特定地点ã§ã®å˜ä¸€ã®äºˆæ¸¬å€¤ã—ã‹å‡ºåŠ›ã™ã‚‹ã“ã¨ãŒå‡ºæ¥ãšã€ãã®åˆ†å¸ƒã‚’把æ¡ã™ã‚‹ã“ã¨ã¯å˜ç´”ã§ã¯ã‚ã‚Šã¾ã›ã‚“。 # sci
Self-Attention Generative Adversarial Networks
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover,
BPEmb
BPEmb: Subword Embeddings in 275 Languages Benjamin Heinzerling and Michael Strube What is this? BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia. Its intended use is as input for neural models in natural language processing. tl;dr Subwords allow guessing the meaning of unknown / out-of-vocabulary words. E.g., the
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