【強化学習】実装しながら学ぶA3C【CartPoleで棒立て：1ファイルで完結】 - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? ※2018年06月23日追記 PyTorchを使用した最新版の内容を次の書籍にまとめました。 つくりながら学ぶ! 深層強化学習 ~PyTorchによる実践プログラミング~ 18年6月28日発売 2016年に発表された強化学習のアルゴリズム「A3C」を実装しながら、解説します。 （エイ・スリー・シー）と呼ぶそうです。 A3Cは、アルファ碁ゼロをはじめ、最新の強化学習を学ぶうえで、避けては通れない重要なアルゴリズムです。 世界一分かりやすいA3C、猫でもわかるA3Cの紹介を目指して、記事を書きます。 ※ 171115 tarutoさまにお気
スプラトゥーン2をdeep learningで攻略してみなイカ？ - mirandora.com
世間は久々の天気の中、3連休ですが、みなさんイカがお過ごしでしょうか。 ある人はバイトに勤しみ、ある人はロブズ・10・プラーでフードを買い、 ある人はバッテラストリートに出かけて塗りたくっているでしょう。 そう、スプラトゥーン2の話です。 スプラトゥーンは、言わずとも知れたNintendo WiiUおよび、Switchにおける超人気ゲームでして、 インクを打ち合うアクションシューティングゲームで、 シリーズ累計で国内270万本、世界590万本の売上本数（2017年9月時点）となるようです。 私の職場のデータサイエンス系の部署でも、80%以上の人がやっておりまして、 もはやSQL、Python、Splatoonが必須のスキルの部署になってしまいました。 さて、スプラトゥーンは対戦ゲームのため、 公式・非公式含め、大会が頻繁に開催されております。 先日、私も初めて大会に参加してきました。 結果
MariFlow - Self-Driving Mario Kart w/Recurrent Neural Network
I trained a recurrent neural network to play Mario Kart human-style. MariFlow Manual & Download: https://docs.google.com/document/d/1p4ZOtziLmhf0jPbZTTaFxSKdYqE91dYcTNqTVdd6es4/edit?usp=sharing Mushroom Cup: https://www.twitch.tv/videos/183296063 Flower Cup: https://www.twitch.tv/videos/183296268 Star Cup: https://www.twitch.tv/videos/183296400 SethBling Twitter: http://twitter.com/sethbling Set
Backpropagation, intuitively | Deep Learning Chapter 3
What's actually happening to a neural network as it learns? Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: http://3b1b.co/nn3-thanks Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks And by CrowdFlower: http://3b1b.co/crowdflower
Gophercises
Gophercises is free, but you need to provide a working email address to gain access. I won't spam you and unsubscribing is very easy. We've all been there before... You are just starting to pick up a new programming language (like Go!) and things are going great. The tutorials are clicking and you are making great progress... and then you run out of tutorials. "What should I build next?" you ask,
Pyro
NumPyro Release We’re excited to announce the release of NumPyro, a NumPy-backed Pyro using JAX for automatic differentiation and JIT compilation, with over 100x speedup for HMC and NUTS! See the examples and documentation for more details. Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive de
The Devil Is in the Detail of Deep Learning Hardware
To identify skin cancer, perceive human speech, and run other deep learning tasks, chipmakers are editing processors to work with lower precision numbers. To identify skin cancer, perceive human speech, and run other deep learning tasks, chipmakers are editing processors to work with lower precision numbers. These numbers contain fewer bits than those with higher precision, which require heavier l
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual concepts from only a handful of examples, similar to the manner in which humans learns
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks witho
これから強化学習を勉強する人のための「強化学習アルゴリズム・マップ」と、実装例まとめ - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? ※2018年06月23日追記 PyTorchを使用した最新版の内容を次の書籍にまとめました。 つくりながら学ぶ! 深層強化学習 ~PyTorchによる実践プログラミング~ 18年6月28日発売 これから強化学習を勉強したい人に向けて、「どんなアルゴリズムがあるのか」、「どの順番で勉強すれば良いのか」を示した強化学習アルゴリズムの「学習マップ」を作成しました。 さらに、各手法を実際にどう実装すれば良いのかを、簡単な例題を対象に実装しました。 本記事では、ひとつずつ解説します。 オレンジ枠の手法は、実装例を紹介します。 ※今回マップを作るに
A multi-branch convolutional neural network for detecting double JPEG compression
Detection of double JPEG compression is important to forensics analysis. A few methods were proposed based on convolutional neural networks (CNNs). These methods only accept inputs from pre-processed data, such as histogram features and/or decompressed images. In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input. Consider
Announcing New AWS Deep Learning AMI for Amazon EC2 P3 Instances
The new AMIs are pre-installed and configured with CUDA 9 for both Ubuntu and Amazon Linux, along with the other GPU drivers to take advantage of the speed of Volta on P3, including cuDNN 7.0, NCCL 2.0.5 and the NVIDIA Driver 384.81. In addition to the latest drivers, the AMI also includes popular frameworks which have been optimized for P3 and Volta including Apache MXNet v0.12 RC1, Caffe2 v0.8.1
Word embeddings in 2017: Trends and future directions
Word embeddings in 2017: Trends and future directions Word embeddings are an integral part of current NLP models, but approaches that supersede the original word2vec have not been proposed. This post focuses on the deficiencies of word embeddings and how recent approaches have tried to resolve them. This post discusses the deficiencies of word embeddings and how recent approaches have tried to res
2018 Intern Results at Preferred Networks (Part 1) - Preferred Networks Research & Development
This summer, Preferred Networks accepted a record number of interns in Tokyo from all over the world. They tackled challenging tasks around artificial intelligence together with PFN mentors. We appreciate their passion, focus, and designation to the internship. In this post, we would like to share some of their great jobs (more to come). Learning to flip objects using guided policy search Takeshi
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A Bayesian Perspective on Generalization and Stochastic Gradient Descent | Deep Learning JP
Reddit - Dive into anything
GitHub - GU-CLASP/TypedFlow: Typed frontend to TensorFlow and higher-order deep learning
GitHub - astorfi/CNTK-World: :earth_americas: Simple and ready-to-use deep learning examples for the Microsoft Cognitive Toolkit (CNTK)
Reddit - Dive into anything
Yu Yamaguchi / 山口 祐 on Twitter: "AlphaGo Zeroが72時間で人間超えてやべーってなってますけど、真にやべーのはこの学習を3日で終わらせるGoogleのマシンパワーですから。 私のPC（GTX 1080 x2）だと、同じ学習を完了するのに62年程度かかります。Master超えバージョンなら735年です。"
