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. The document also discusses using deep reinforcement learning for robot control and how understanding the principles can help in problem setting.
This document contains a summary of 3 papers on deep residual networks and squeeze-and-excitation networks:
1. Kaiming He et al. "Deep Residual Learning for Image Recognition" which introduced residual networks for image recognition.
2. Andreas Veit et al. "Residual Networks Behave Like Ensembles of Relatively Shallow Networks" which analyzed how residual networks behave like ensembles.
3. Jie Hu et al. "Squeeze-and-Excitation Networks" which introduced squeeze-and-excitation blocks to help convolutional networks learn channel dependencies.
The document also references the PyTorch ResNet implementation and provides URLs to the first and third papers. It contains non-English
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. The document also discusses using deep reinforcement learning for robot control and how understanding the principles can help in problem setting.
This document contains a summary of 3 papers on deep residual networks and squeeze-and-excitation networks:
1. Kaiming He et al. "Deep Residual Learning for Image Recognition" which introduced residual networks for image recognition.
2. Andreas Veit et al. "Residual Networks Behave Like Ensembles of Relatively Shallow Networks" which analyzed how residual networks behave like ensembles.
3. Jie Hu et al. "Squeeze-and-Excitation Networks" which introduced squeeze-and-excitation blocks to help convolutional networks learn channel dependencies.
The document also references the PyTorch ResNet implementation and provides URLs to the first and third papers. It contains non-English
Mini Tokyo 3D − 交通デジタルツインとデジタルトランスフォーメーションの世界nagix
みんなのPython勉強会#59: Stapy Global Meetupでの発表資料です。
Mini Tokyo 3D は、昨年度の公共交通オープンデータ使ったアプリコンテスト「第3回東京公共交通オープンデータチャレンジ」の最優秀賞受賞作品です。今回はこのアプリの開発秘話とデジタルツインの可能性についてお話しします。
Stapyの第1回勉強会でデータサイエンス・データエンジニアリングについてお話ししたのですが、その後シンガポールに移住して4年が経ちました。海外の開発現場の経験なども交えて、データの意味付けやデジタルトランスフォーメーションの考え方についても触れたいと思います。
Mini Tokyo 3D は PC、スマートフォン、タブレット、セットトップボックスなど、デバイスを問わず Web ブラウザさえあれば利用できる Web アプリケーションです。下記の URL からアクセスしてください。
https://minitokyo3d.com
より詳しい情報は Mini Tokyo 3D ユーザーガイド https://github.com/nagix/mini-tokyo-3d/blob/master/USER_GUIDE-ja.md をご覧ください。ソースコードは GitHub リポジトリhttps://github.com/nagix/mini-tokyo-3d にて公開されています。開発の経緯は、Mini Tokyo 3D 開発日誌 https://togetter.com/li/1413307 にまとめています。