GitHub - yenchenlin/DeepLearningFlappyBird: Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
第1回 IkaLog誕生前夜 | gihyo.jp
2015年5月に発売された大人気ゲームスプラトゥーン、皆さんはプレイしていますか？ 著者のまわりでは多くのコンピュータエンジニアが楽しんでいます。私は、スプラトゥーンの動画をリアルタイムに分析するソフトウェアIkaLogを開発し、オープンソースとして公開しています。本連載では、IkaLogの開発秘話（？）や画像認識に使っているアルゴリズム、開発の過程などについて紹介します。 今回は連載第1回目として、私（たち）がどのようなきっかけからIkaLogを開発し始めたのかを紹介し、次回以降では具体的にどのような検討を経てIkaLogが実装されてきたかを説明します。 スプラトゥーンとの出会い 私がスプラトゥーンというゲームを知ったのは、発売が数日後に迫った日にインターネット上でトレイラームービーを見かけたときでした。スプラトゥーンは、三人称視点（TPS）でプレイヤーのインクリング（イカ人間）を操作し
Deep Q-Learning (Space Invaders)
Ever since I learned about neural networks playing Atari games I wanted to reimplemnted it and learn how it works. Below you can see an AI playing Space Invaders. I trained it during my batch at Recurse Center on little over 50M frames. It is more awesome if you realize that the AI was trained in a similar way a human would learn: the only inputs are screen and number of gained (or lost) points af
Gneural Network - GNU Project - Free Software Foundation
This project has been decommissioned. This web page is kept here for historical purposes only. Introduction Gneural Network is the GNU package which implements a programmable neural network. The current version, 0.9.1, has the following features: A scripting language is available which allows users to define their own neural network without having to know anything about coding. Advanced programmer
Deep Learning Tokyo 2016 in東京 - パスマーケット
TOP › セミナー、ビジネス Deep Learning Tokyo 2016 2016/3/20(日) 13:20～2016/3/20(日) 19:00 イベント受付開始時間 2016/3/20(日) 12:50～ 東京ミッドタウンタワー11F（Yahoo! JAPAN本社） 2016/3/20 15:39 追加 14:30～17:00はミッドタウンタワー2F受付へのエスカレーターが停止しております。 遅れてご参加される場合は「ds-event-info@ml.yahoo-corp.jp」までご一報いただき、 ミッドタウンタワー1F、総合受付付近でお待ちください。係の者が伺います。 2016/2/25 20:04 追加 イベント概要 ======================================== Deep Learningに携わる実務者の方々、日頃からCaffeやCh
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose.
論文輪読
A review of unsupervised feature learning and deep learning for time-series modeling 那須野 薫 2015年4月16日 東京大学松尾研究室 紹介する論文について • タイトル： – A review of unsupervised featu
論文輪読 A review of unsupervised feature learning and deep learning for time-series modeling 那須野 薫 2015年4月16日 東京大学松尾研究室 紹介する論文について • タイトル： – A review of unsupervised feature learning and deep learning for time-series modeling – 時系列モデリングのための教師なし表現学習とディープラー ニングに関する調査 • 著者： – Martin L., Lars K., Amy L. – Örebro University in Sweden • 被引用回数：12 • 引用件数：135 • 公開年：2014 2015年4月16日東京大学松尾研究室 那須野薫
Deep Learning for Monaural Source Separation
Monaural source separation is important for many real world applications. It is challenging in that, given only single channel information is available, there is an infinite number of solutions without proper constraints. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including the monaural speech separatio
１枚の紙から切り出したペーパークラフト
취향저격한 구독 상품을 한눈에 모아보고 알림받는 내구독 북마크한 콘텐트와 내활동을 아카이빙하는 보관함 기억하고 싶은 문구를 스크랩하고 기록하는 하이라이트/메모 중앙일보 회원에게만 제공되는 스페셜 콘텐트
New Guide on Creating an App & Making Money | Bilue
Experience Design Mobile Apps Cloud Engineering Enterprise Integrations Web Development Smart TV Contact Us Why Mobile Apps?If you are reading this, then you have most likely heard of the app craze that has taken over in recent years. A record 218 billion apps were downloaded last year while consumer spending leaped 20% to hit $143 billion, for the first time. The need to create and make money wi
Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a s
Q&A: Andrew Ng, chief scientist, Chinese search giant Baidu
Artificial intelligence whiz Andrew Ng hangs his hat these days at a nondescript building in Sunnyvale that serves as the Silicon Valley outpost of the Chinese search giant Baidu. The modest digs belie Baidu’s big Asian footprint. With more than 600 million monthly active mobile users, it’s often referred to as “the Google of China.” Like Google, Baidu has been exploring artificial intelligence fo
Yahoo open-sources CaffeOnSpark deep learning framework for Hadoop
Yahoo open-sources CaffeOnSpark deep learning framework for Hadoop Yahoo today is releasing some key artificial intelligence software (AI) under an open-source license. The company last year built a library called CaffeOnSpark to perform a popular type of AI called “deep learning” on the vast swaths of data kept in its Hadoop open-source file system for storing big data. Now it’s becoming availabl
All you need is a good init
Layer-sequential unit-variance (LSUV) initialization - a simple method for weight initialization for deep net learning - is proposed. The method consists of the two steps. First, pre-initialize weights of each convolution or inner-product layer with orthonormal matrices. Second, proceed from the first to the final layer, normalizing the variance of the output of each layer to be equal to one. Expe
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entir
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
How do I derive the back-propagation algorithm in Convolutional Neural Network?
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
Hadoop, Spark, Deep Learning Mesh on Single GPU Cluster
Cracking AWS Solution Architect Professional Certification
GitHub - icoxfog417/rnn_demo: Recurrent Neural Network Demo by PyBrain
