Sensors, actuators and the Raspberry PI using PythonDerek Kiong
![Micro Python で組み込み Python](https://cdn-ak-scissors.b.st-hatena.com/image/square/f8c4ac7e65755f2279716c2b5d44a81412b3a30c/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2Fmicropython-140913020532-phpapp01-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)
This document introduces deep reinforcement learning and provides some examples of its applications. It begins with backgrounds on the history of deep learning and reinforcement learning. It then explains the concepts of reinforcement learning, deep learning, and deep reinforcement learning. Some example applications are controlling building sway, optimizing smart grids, and autonomous vehicles. T
CSS組版の文献紹介と作業報告(Vivliostyle新年会) 1. CSS 組版の文献紹介と作業報告 妹尾 賢 (SENOO, Ken) @senopen Vivliostyle 新年会 2015-01-07 This work is licensed under the Creative Commons Attribution 4.0 International License except for images and figures. https://www.facebook.com/events/391498301016858/ 2. 2 内容 ■なぜここにいるか ■CSS 組版に関する内容 ➤CSS 組版の文献紹介 ➤HTML+CSS+JavaScript の参考図書 ■CSS 組版のお試し ➤表組み ➤相互参照 ■課題と今後 3. 3 自己紹介 minimal size: 1
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models. 2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representatio
Common Lisp製のテキストエディタにコントリビューションをしたので、その実装とかこぼれ話を発表しました。
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