ConvNetJS: Deep Learning in your browser
Introduction There are two ways to use the library: inside the browser, or on a server using node.js. In-browser Use: Skeleton Code and Setup You might find this section useful if you are relatively new to web development. The fastest way to obtain the library in a plug-and-play way if you don't care about developing is through this link to convnet-min.js, which contains the minified library. Alte
DattoCon
DattoCon is an open-ecosystem experience supercharged with content catered to everyone at every MSP, from tech to exec. Why Attend? At DattoCon, MSPs can immerse themselves in tracks covering everything from technology trends to business best practices. Connect with and learn from your peers, industry thought leaders, and top vendors from across the channel. Experience what makes DattoCon the most
GitHub - Lasagne/Lasagne: Lightweight library to build and train neural networks in Theano
Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as Convolutional Neural Networks (CNNs), recurrent networks including Long Short-Term Memory (LSTM), and any combination thereof Allows architectures of multiple inputs and multiple outputs, including auxiliary classifiers Many optimization methods including Nes
ã€åºƒå‘Šæ¥ç•Œãƒ‹ãƒ¥ãƒ¼ã‚¹å·å¤–】アドテクãƒãƒ–ルã‹ã‚‰ç›®ãŒé†’ã‚ã¦è‹¦ã—ã„ã®ã¯ã¾ãšDSPã€ãã—ã¦å›½å†…プãƒã‚°ãƒ©ãƒžãƒ†ã‚£ãƒƒã‚¯å¸‚å ´ãŒè¯é–‹ãã‚«ã‚®ã¯ãƒ‘ブリッシャートレーディングデスクã«ã‚り。 - ã˜ãŽã‚‡ãƒ¡ãƒ¢
ãƒãƒ§ãƒˆãƒžãƒ†ãƒãƒ§ãƒˆãƒžãƒ†ã‚ªãƒ‹ã‚¤ã‚µãƒ¼ãƒ¼ãƒ³ 「急ã§ã™ã¾ãªã„ã®ã ãŒæœ¬æ—¥ä¸ã«ã“れをよã‚ã—ã〠ッテナンデスノーーン。 本日言ã†ãŸã‚‰ã‚‚ã†ã‚ã¨7時間 ã‚‚ã€ã‚る。（真顔） ãŠç–²ã‚Œæ§˜ã§ã™ã€‚高橋ã§ã™ã€‚ DSPå¸‚å ´ã«ã¤ã„ã¦æ›¸ãã¾ã—ãŸã€‚（ã®ã‚’社内å‘ã‘ã ã£ãŸã®ã§ãƒªãƒ©ã‚¤ãƒˆã—ã¦è»¢è¼‰ã§ã™ï¼‰ www.kccs.co.jp KANADE | DSP主ãªç‰¹é•· ãªã‚“ã ãã‚Œãã‚“ãªã«ãƒ‡ã‚«ã„DSPãŒã„ããªã‚Šå‡ºæ¥ã‚‹ã‚“ã‹ãƒ¼ãƒ¼ãƒ¼ã„ã€ã£ã¦ã„ã†ã‹ãƒ‡ã‚¯ãƒ¯ã‚¹ã¯ã©ãƒ¼ãƒ¼ã™ã‚“ã˜ã‚ƒãƒ¼ãƒ¼ãƒ¼ã„ã¨æ€ã„ãã‚„ã€ãƒ‡ã‚¯ãƒ¯ã‚¹ã®åå‰ãŒå¤‰ã‚るよã†ã§ã™ã。ãªã‚“ã‚„ãん。 KCCSã•ã‚“実ã¯DMPãªã©ã®ãƒ©ã‚¤ãƒ³ãƒŠãƒƒãƒ—ã‚‚ã‚ã‚‹ã—海外展開も（最近部署ã¾ã§ä½œã£ã¦ï¼‰é ‘張るã¨ã®ã“ã¨ã§ã€å’Œé¢¨ãƒãƒƒã‚¯ãªçµ±ä¸€ãƒ–ランドãƒãƒ¼ãƒ ãŒã»ã—ã‹ã£ãŸã®ã§ã—ょã†ã‹ã€‚ 外資系SSPã¨é€£æºã‚‚ã—ã¦ã€é…信先を拡大ã—ãŸã¨ã®ã“ã¨ã€‚ ã“ã£ã‹ã‚‰ãŒæœ¬é¡Œã€‚ ã¶ã£ã¡ã‚ƒã‘ã€å€‹äººçš„ã«DSP界隈ã«å¯¾ã—ã¦æ€ã†ã“ã¨ã¯ã€ã‚‚ã†ç´°ã‹ã„é…ä¿¡å…ˆã®å¤šã•ã¨ã‹ç›®æ–°ã—ã„ター
Jupyter Notebook Viewer
Otto Group Product Classification Challenge using nolearn/lasagne¶ This short notebook is meant to help you getting started with nolearn and lasagne in order to train a neural net and make a submission to the Otto Group Product Classification Challenge. Otto Group Product Classification Challenge Get the notebook from the Otto Group repository Nolearn repository Lasagne repository A nolearn/lasagn
Gensim: topic modelling for humans
Documentation¶ We welcome contributions to our documentation via GitHub pull requests, whether it’s fixing a typo or authoring an entirely new tutorial or guide. If you’re thinking about contributing documentation, please see How to Author Gensim Documentation. Core Tutorials: New Users Start Here!¶ If you’re new to gensim, we recommend going through all core tutorials in order. Understanding this
伊勢丹新宿店ã®äººæ°—手土産。フードアテンダントã®ãŠã™ã™ã‚10é¸ | 三越伊勢丹ã®é£Ÿãƒ¡ãƒ‡ã‚£ã‚¢ | FOODIE（フーディー）
海外ã‹ã‚‰ã®åˆå‡ºåº—ã€äººæ°—ブランドや話題ã®ã‚·ãƒ§ãƒƒãƒ—ã®æ–°å•†å“ãªã©ã€ã‚¢ã‚¤ãƒ†ãƒ ãŒæ—¥ã€…æ›´æ–°ã•ã‚Œã‚‹ã‚¹ã‚¤ãƒ¼ãƒ„シーン。ã§ã‚‚ã€æ•°ãˆåˆ‡ã‚Œãªã„é¸æŠžè‚¢ã®ãªã‹ã§ã€ã€Œæ°—ã®åˆ©ã„ãŸã€æ‰‹åœŸç”£ã£ã¦ã€ãªã‹ãªã‹è¦‹ã¤ã‹ã‚‰ãªã„ã‚‚ã®ã€‚ ã—ã‹ã‚‚ã€è€èˆ—ã‚„å店ã®é€¸å“ã‹ã‚‰ã€æœ€æ–°ã®ãƒˆãƒ¬ãƒ³ãƒ‰ã‚¢ã‚¤ãƒ†ãƒ ã¾ã§ã€ã•ã¾ã–ã¾ãªã‚¹ã‚¤ãƒ¼ãƒ„ãŒé›†ã¾ã‚‹æ±äº¬ã§ã¯ã€ã©ã‚Œã‚’é¸ã¹ã°ã„ã„ã®ã‹è¿·ã†ã“ã¨ã‚‚å°‘ãªãã‚ã‚Šã¾ã›ã‚“。 ãã“ã§ã€ä¼Šå‹¢ä¸¹æ–°å®¿åº—フードアテンダントã®åŠ 藤美紀ã•ã‚“ã«ã€ã„ã¾è´ˆã‚ŠãŸã„ãŠã™ã™ã‚ã®æ‰‹åœŸç”£10点を教ãˆã¦ã‚‚らã„ã¾ã—ãŸã€‚ 手土産ã§äººæ°—ã®æ´‹è“åジャンルã‹ã‚‰6アイテム①＜フィーカ＞手土産ã®æ–°å®šç•ªã«ãªã‚Šãã†ãªåŒ—欧ã®ãŠè“å 「ãªã‚“ã¨è¨€ã£ã¦ã‚‚ã€ç®±ã®ã‹ã‚ã„ã•ãŒé…力。国内出店ã¯ä¼Šå‹¢ä¸¹æ–°å®¿åº—ã®ã¿ã§ã™ã®ã§ã€å…¨å›½ã§ã“ã“ã—ã‹è²·ãˆãªã„ã¨ã„ã†ã€Žç‰¹åˆ¥æ„Ÿã€ã‚‚å分。1,080円ã¨ã„ã†ãŠå€¤æ®µã®æ‰‹é ƒã•ã‚‚大事ãªãƒã‚¤ãƒ³ãƒˆã§ã™ã〠箱入りクッã‚ーã¯ä½™ã£ã¦ã—ã¾ã„ãŒã¡ã§ã™ãŒã€é£Ÿã¹åˆ‡ã‚Šã‚„ã™ã„サイズも好å°è±¡ã€‚ç®±ã¯å¸¸æ™‚
Martin Sorrell on What’s Next
Sir Martin Sorrell acquired his way into the advertising business during the 1980s, first by scooping up small agencies and then by stunning the ad world with takeovers of J. Walter Thompson and Ogilvy & Mather. By the mid-1990s his company, WPP, was a dominant force in advertising, and Sorrell himself saw the future of the business as going digital. In 1996, when the idea that the web could chang
ランã‚ング
ランã‚ング
ランã‚ング
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
graphlab.boosted_trees_classifier.create — GraphLab Create API 1.3 documentation
Otto Group Product Classification Challenge
ã€æ¥½å¤©å¸‚å ´ã€‘ã‚®ãƒ•ãƒˆã‚»ãƒƒãƒˆÂ > プレミアムギフト：Myレシピ 毎日トクã ãP＆Gマート
Kauliæ ªå¼ä¼šç¤¾ã®æ ªå¼å–得（連çµå会社化）ã«é–¢ã™ã‚‹ãŠçŸ¥ã‚‰ã› | æ ªå¼ä¼šç¤¾VOYAGE GROUP
(VIDEO) Sir Martin Sorrell on Transformation of Advertising and of WPP
{{#tags}}- {{label}}
{{/tags}}