DLIF tutorial
Tutorial in Thirty-first Conference on Artificial Intelligence (AAAI-17) February 5 (9:00 AM – 1:00 PM), 2017, San Francisco, California, USA Slides Part1: Basics of neural networks(Feb. 3, 2017 Updated) Part2: Common design of neural network implementations(Mar. 30, 2017 Updated1) Part3: Differences of deep learning frameworks1 Fixed the backprop design choice of MXNet (Mar. 30, 2017) Coding Exam
2015 Tech Trends from the Future Today Institute (formerly Webbmedia …
2015 Tech Trends from the Future Today Institute (formerly Webbmedia Group Digital Strategy) 1. 2015 TREND REPORT Disruptive technologies that will affect consumer behavior and impact your business strategy in the coming year. THE FUTURE TODAY INSTITUTE 2. ABOUT THE FUTURE TODAY INSTITUTE The Future Today Institute is the leading digital strategy consulting firm for emerging technology advising in
Official Google Research Blog: Machine Learning with Quantum Algorithms
Posted by Hartmut Neven, Technical Lead Manager Image Recognition Many Google services we offer depend on sophisticated artificial intelli...
tl;dr: Check it out at parkorbird.flickr.com! We at Flickr are not ones to back down from a challenge. Especially when that challenge comes in webcomic form. And especially when that webcomic is xkcd. So, when we saw this xkcd comic we thought, “we’ve got to do that”: In fact, we already had the technology in place to do these things. Like the woman in the comic says, determining whether a photo
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(2001) 49 1 23–42 —– —– NTT * 2000 10 16 2001 4 23 1. 1.1 1998 * 104–0033 1–21–2 7F 24 49 1 2001 1999 70 91 MIT M. Turk “Recognition Using Eigenface” (Turk and Pentland (1991)). 1998 IC 1 CPU (Jain and Waller (1978), Raudys (1981)) 1997 Chellapa et al. 1995 Pentland and T. Choudbury 2000 25 1.2 CCD CRT � x Gabor filter � x � x 2 2 NTTdata NTT 79 10 10 16 × 12 26 49 1 2001 1. NTTdata 2. UMIST 1 UMI
MS の Deep Learning Framework CNTK で画風変換～もしも小学生の自分にゴッホを描かせたら？ - Qiita
MS の Deep Learning Framework CNTK で画風変換～もしも小学生の自分にゴッホを描かせたら？Azure機械学習DeepLearningKerasCNTK Microsoft の Deep Learning のフレームワーク Cognitive Toolkit (CNTK) が、2016 年 10 月に Version 2.0 に上がって Python でも使えるようになりました。そこからさらに活発に開発が行われてどんどん進化しているようです。 Update (2017/04/05): 4/4 に CNTK がついに beta が取れて、V 2.0 Release Candidate 1 に Version が上がりました！詳しくはこちらから！なので、それに伴って環境構築方法を更新しました。 Update (2017/04/23): 先日、V 2.0 RC 2 に
Recognizing and Localizing Endangered Right Whales with Extremely Deep Neural Networks
In this post I’ll share my experience and explain my approach for the Kaggle Right Whale challenge. I managed to finish in 2nd place. 1. Background Right whale is an endangered species with fewer than 500 left in the Atlantic Ocean. As part of an ongoing preservation effort, experienced marine scientists track them across the ocean to understand their behaviors, and monitor their health condition.
Flowers are a great way to show someone you care. They can make a person smile and brighten up their day. There are many florists in Auckland that you can choose from. They can also provide gifts for any occasion. For big, beautiful bouquets that look like they stepped out of a rom-com, head to Nina for Flowers in Grey Lynn. Their flower subscription service delivers market-fresh blooms straight t
As the 2010’s draw to a close, it’s worth taking a look back at the monumental progress that has been made in Deep Learning in this decade.[1] Driven by the development of ever-more powerful compute and the increased availability of big data, Deep Learning has successfully tackled many previously intractable problems, especially in Computer Vision and Natural Language Processing. Deep Learning has
20180917 Argentina in Python: community, dreams, travels and learning 東大松尾研流 実践的AI人材育成法 実践・競馬データサイエンス Why you should care about types: Python Typing in the Facebook Backend Applying serverless architecture pattern to distributed data processing Webアプリケーションの仕組み DjangoではじめるPyCharm実践入門 PyCon JP における子ども向けワークショップの活動事例と実施の意義 Building Maintainable Python Web App using Flask オンザフライ高速化パッケージの比較：Numba, Tenso
A powerful new class of large language models is making it possible for machines to write, code, draw and create with credible and sometimes superhuman results. Humans are good at analyzing things. Machines are even better. Machines can analyze a set of data and find patterns in it for a multitude of use cases, whether it’s fraud or spam detection, forecasting the ETA of your delivery or predictin
Deploying Transformers on the Apple Neural Engine
An increasing number of the machine learning (ML) models we build at Apple each year are either partly or fully adopting the Transformer architecture. This architecture helps enable experiences such as , , , , and many others. This year at WWDC 2022, Apple is making available an open-source reference PyTorch implementation of the Transformer architecture, giving developers worldwide a way to seaml
Meredith Whittaker has had it with the “frat house” contingent of the tech industry. I sat down with the CEO of Signal at VivaTech in Paris to go over the wide range of serious, grown-up issues society is facing, from disinformation, to who controls AI, to the encroaching surveillance state. In the course of our…
こんにちは、小澤です。 Amazon SageMaker(以下SageMaker)のビルドインアルゴリズムには、Image Classification(画像分類)があります。 これは、ResNet[1]というDeep Learningを使った手法になっており、とても高い精度での分類が期待できます。 SageMakerではこのImage Classificationの利用方法を解説したexampleが存在してます。 Image-classification-fulltraining.ipynb このexampleはCaltech256[2]の学習を行うものになっています。 なお、この他にもImage Classificationではこの他にImageNet[3]を利用した転移学習も対応しており、同じディレクトリにそのexampleも存在しています。 さて、このexampleはboto3を利
EasySpider: No-Code Visual Web Crawler/Browser Automation Test Tool
BrightData is the market leader in the proxy industry, covering 72 million IPs worldwide, offering real residential IPs, instant batch collection of publicly available web data, with a guaranteed high success rate. For those in need of high cost-performance proxy IPs, click on the image above to register and contact the Chinese customer service. After activation, you get a free trial and up to $25
FPGAX Google@六本木にて． https://fpgax.connpass.com/event/115446/ FPGAXメモ 「TPUの最近の話」Google 佐藤さん TPU Pod - HPC-powered scalable all reduce distributed training Cloud TPU - https://cloud.google.com/tpu/ 実はいろんなHWがある．BigQuery architectureとかもある https://cloud.google.com/solutions/architecture/complex-event-processing https://cloud.google.com/blog/products/gcp/implementing-an-event-driven-architecture-on-serv
Welcome to Startups Weekly — Haje‘s weekly recap of everything you can’t miss from the world of startups. Sign up here to get it in your inbox every Friday. Well,…
3つの要点 ✔️ 画像分類タスクに必要不可欠なData Augmentationの体系をまとめた ✔️ 基本的なData Augmentationについて手法と利点/欠点をまとめた ✔️ 基本的なDAは実装が簡単な上に絶大な効果を発揮する A survey on Image Data Augmentation for Deep Learning written by Connor Shorten, Taghi M. Khoshgoftaar (Submitted on 06 July 2019) Comments: Published by Journal of Big Data Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Proc
TC50: Sekai Camera for Social Tagging on the iPhone | TechCrunch
Sekai Camera (World Camera in Japanese) is an iPhone-exclusive social tagging service developed by Tokyo-based mobile application provider Tonchidot. The company’s TechCrunch50 presentation (and following Q&A) was pretty hard to understand because of the language barrier but Sekai Camera turned out to be a crowd-pleaser nonetheless. The key idea is to use the iPhone as a mobile information termina
We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep learning, a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolutio
2014年12月23日 編集：資料・動画の埋め込みは重かったのでリンクにし、いくつかの資料へのリンクを追加しました。 毎回行きたいと思いながらも用事があって行けなかったPyConに初めて参加しました。 感想 Kenneth Reitzさんによる1日目の基調講演は、言語、そしてインターネットによって人は他の人と交流できるようになったのに、Pythonは2と3の二つの言語に分かれてしまっているという話でした。 個人的にはUnicode周りが面倒なPython 2にはもう戻れないと感じていますが、利用状況は2のほうが圧倒的でした。確かに英語圏の人にはメリットが感じにくいので、メリットを享受しやすい非英語圏（含む日本）の人が積極的にPython 3に移行していくと幸せになれるのではないでしょうか。 西尾泰和さんによる2日目の基調講演は、人間は人工物、言語、方法論、教育の4要素によって増強することが
Team A company built by a great team with seamless vision for the future. Careers We believe in people and always looking for great minds with passion for AI and tech. Blog Learn from industry experts in machine learning and read insightful analysis. Research Publications Resource of scientific and academic use of Imagga’s AI technologies. Projects Internal AI lab for computer vision related proje
自分の研究室は自然言語処理を扱っている研究室で、自分自身も自然言語を扱った研究をしたいと考えているのですが、 「CNNの気持ちを理解できるかな？」と思いComputer Visionで有名そうな論文を何本か読んでみたので、 日記をつける程度の感覚でものすごく簡単にまとめてみようと思います。どんな論文を読んでみようか迷っている方のお役に立ったとしたら幸いです。 初学者につき間違い等ありましたら、コメントなどでご教授いただければ幸いです。 ImageNet Classification with Deep Convolutional Neural Networks (link) おそらくこれはDeepLeaningが盛り上がり始めた頃の論文ですね。 これを読んでみた理由としては、学習に使用するデータの正規化等の方法が他の論文から多く引用されているのを発見したからです。 Local Respon
![最近読んだCNN系論文ざっくりまとめ [ 画像編 ] - 電通生のにこにこ調布日記](https://cdn-ak-scissors.b.st-hatena.com/image/square/f53cb93806aa783d57d430bf7abbaa3171ab4285/height=288;version=1;width=512/https%3A%2F%2Fogimage.blog.st-hatena.com%2F10328749687244679943%2F10328749687244705853%2F1495760910)
3つの要点 ✔️ 深層学習を用いたData AugmentationにはGANやスタイル変換などを用いたものがある ✔️ 深層学習を用いたDAの利点/欠点をまとめた ✔️ 基本的なDAと組み合わせることでさらに高い精度を達成できる A survey on Image Data Augmentation for Deep Learning written by Connor Shorten, Taghi M. Khoshgoftaar (Submitted on 06 July 2019) Comments: Published by Journal of Big Data Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processi
Google Cloud Machine Learning family grows with new API, editions and pricing | Google Cloud Blog
Google Cloud Machine Learning family grows with new API, editions and pricing Google Cloud Machine Learning is one of our fastest growing products areas. Since we first announced our machine learning offerings earlier this year, we’ve released a steady stream of new APIs, tools and services to help you harness the power of machine learning. We’ve seen machine learning transform users’ experiences,
総合研究大学院大学 複合科学研究科 情報学専攻 卒 博士（情報学） 自然言語処理や機械学習、データ分析に関する研究内容とwebシステムの開発と運用について書いています。 シリコンバレーベンチャーみたいに深い技術の事業化をしたいと思っています。 ご興味ある方はご連絡ください。 実験してみて、案外使い道がたくさんありそうな気がしてきました SURF: Speeded Up Robust Features Herbert Bay, Tinne Tuytelaars, and Luc Van Gool Katholieke Universiteit Leuven Computer Vision - ECCV 2006 The task of finding correspondences between two images of the same scene or object is part
Google Experiments With Next Generation Image Search | TechCrunch
Two Google scientists presented a paper (pdf embedded below) at the World Wide Web Conference in Beijing last week that outlines their vision for the future of image search. Notably, the new image search technology doesn’t just index text associated with an image in determining what’s in it. Google is now talking about using computers to analyze the stuff in photos, and using that to associate it
Very Deep Convolutional Networks for Large-Scale Visual Recognition - Visual Geometry Group Home Page
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Karen Simonyan and Andrew Zisse
先に成果から 判定をミスった画像 判定精度は99.58% 10,000枚のテストデータセットのうち42枚がエラーとなりました。 画像の下にセンタリングされている数字が正解ラベル。その下に左寄せで5つ表示されている数字は尤度が高い順に並んだ予想。 尤度に比例した棒グラフが表示されますが、殆どのケースで尤度が低すぎて棒グラフになっていません。 多くのものは正解が2番めの尤度(第2候補)になっています。 しかし、右上の3枚のように正解が5番目(第5候補)までに入らないほど酷くハズしてしまうこともあるようです。 数字別に最も尤度が高かった画像 500Epochの学習後に行ったテストにて、数字別に最も尤度が高かった画像を抽出しました。 換言すれば「最も自信を持って正解した画像」と言って良いと思います。 でも「最もそれらしい」と言うよりは「他に似ていない」ものが選ばれている感もありますが... 特に6
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Neural Networks and Deep Learning What this book is about On the exercises and problems Using neural nets to recognize handwritten digits How the backpropagation algorithm works Improving the way neural networks learn A visual proof that neural nets can compute any function Why are deep neural networks hard to train? Deep learning Appendix: Is there a simple algorithm for intelligence? Acknowledge
Today, we announced our latest product version, SingleStoreDB Self-Managed 6.7. With this release, SingleStore is now free for everyone to use for databases with up to 128GB of RAM usage, and no limit on database size on disk, including solid state drive (SSD). Unlike what customers get from other database providers, the free tier of SingleStore is full featured and includes all enterprise capabil
State of the Internet Operating System Part Two: Handicapping the Internet Platform Wars - O'Reilly Radar
State of the Internet Operating System Part Two: Handicapping the Internet Platform Wars This post is Part Two of my State of the Internet Operating System. If you haven’t read Part One, you should do so before reading this piece. As I wrote last month, it is becoming increasingly clear that the internet is becoming not just a platform, but an operating system, an operating system that manages acc
In the previous part of this tutorial, I introduced a bit of Ruby TensorFlow and showed a few examples of using Ruby Tensorflow. In this part, I will dwell upon simple and useful ideas that will help developers understand how google protobuf is used in Ruby Tensorflow. Even though the primary purpose of the blog post is to explain the Ruby API, I am sure that developers specializing in different l
GitHub - diff-usion/Awesome-Diffusion-Models: A collection of resources and papers on Diffusion Models
A Survey on Video Diffusion Models Zhen Xing, Qijun Feng, Haoran Chen, Qi Dai, Han Hu, Hang Xu, Zuxuan Wu and Yu-Gang Jiang arXiv 2023. [Paper] 16 Oct 2023 State of the Art on Diffusion Models for Visual Computing Ryan Po, Wang Yifan, Vladislav Golyanik, Kfir Aberman, Jonathan T. Barron, Amit H. Bermano, Eric Ryan Chan, Tali Dekel, Aleksander Holynski, Angjoo Kanazawa, C. Karen Liu, Lingjie Liu, B
CAPTCHA - Wikipedia
This article may need to be rewritten to comply with Wikipedia's quality standards, as It feels like an essay criticising CAPTCHA. You can help. The talk page may contain suggestions. (November 2022) This CAPTCHA (Version 1[clarification needed]) of "smwm" obscures its message from computer interpretation by twisting the letters and adding a slight background color gradient. A CAPTCHA (/ˈkæp.tʃə/
Amazon EC2 instance types - Amazon Elastic Compute Cloud
When you launch an instance, the instance type that you specify determines the hardware of the host computer used for your instance. Each instance type offers different compute, memory, and storage capabilities, and is grouped in an instance family based on these capabilities. Select an instance type based on the requirements of the application or software that you plan to run on your instance. Am
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Kern Baby Kern It has been predicted that Apple will have sold 45 million iPhones by the end of 2009. And that’s before it hits China. There aren’t hundreds of type-related apps for the iPhone, but here are few; and a few type-related tips too. (Helvetica Moleskine give-away details at the end). iPhone apps Recently released, MyFonts’ What The Font for iPhone is a terrific little app. The biggest
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パターン認識・メディア理解研究会 2月17日 大規模一般画像認識と画像表現 Large-Scale Generic Image Recognition and Image Representation and Image Representation 東京大学/JSTさきがけ 原田達也 1 Flickr reached 5,000,000,000 photos on September 19, 2010. http://blog.flickr.net/en/2010/09/19/5000000000/ 2 The Growth of Flickr • Over 5,000,000,000 photos • 4,596 uploads in the last minute in last minute • 134,362,183 geotagged items http://
Releasing the World’s Largest Street-level Imagery Dataset for Teaching Machines to See
Today we present the Mapillary Vistas Dataset—the world’s largest and most diverse publicly available, pixel-accurately and instance-specifically annotated street-level imagery dataset for empowering autonomous mobility and transport at the global scale. Pixel-wise, instance-specific annotations from the Mapillary Vistas Dataset (click on an image to view in full resolution) Since we started our e
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Introducing: Flickr PARK or BIRD | code.flickr.com
CSS3 Wizardry - Putting CSS3 and HTML5 to Work
The Decade of Deep Learning
（作成中）PyConJP2018 資料一覧 - Qiita
Generative AI: A Creative New World
TechCrunch | Startup and Technology News
SageMaker Python SDKを使った画像分類 | DevelopersIO
Diary/2019-2-2 - Miyo's Page
TechCrunch | Startup and Technology News
【基本編】画像認識に使用されるData Augmentationを一挙にまとめてみた！
Deep Image: Scaling up Image Recognition
PyCon JP 2014に参加しました - orangain flavor
Image Recognition API, Computer Vision AI – Imagga
最近読んだCNN系論文ざっくりまとめ [ 画像編 ] - 電通生のにこにこ調布日記
【応用編】深層学習を用いた画像Data Augmentationを一挙にまとめてみた！
画像の部分的類似性検出 : 研究開発
TensorFlowでVGG19を使ってMNISTのエラー画像一覧を作ってみた - Qiita
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Ruby Tensorflow for developers