Feacbookドックが書いてない説明:ReactJS/GraphQL/RelayJS. Talk at Tokyo React.js Meetup #3 (http://reactjs-meetup.connpass.com/event/26229/)
English version: http://www.slideshare.net/KhorSoonHin/tokyo-reactjs-3-missing-pages-reactjsfluxgraphqlrelayjs
Tokyo React.js #3: Missing Pages: ReactJS/Flux/GraphQL/RelayJSKhor SoonHin
Missing Pages: ReactJS/Flux/GraphQL/RelayJS. Shed light on assumptions/details glossed over by Facebook docs. Presented at Tokyo React.js Meetup #3 (http://reactjs-meetup.connpass.com/event/26229/)
Japanese version: https://www.slideshare.net/KhorSoonHin/reactjs-3-meetup-ja
Video: https://youtu.be/YFuQlKBXlmA
Described the use of Root Mean Square Logarithmic Error (RMSLE) as cost function in machine learning (ML).
1. What is it?
2. How is it different from RMSE?
3. When to use it?
From Back to Front: Rails To React FamilyKhor SoonHin
ReactJS, Flux, RelayJS, GraphQL, challenges the way we think & code front-end. This presentation explains what they are, how they work together, and how to get them to work on Rails.
In React/Flux, every time a user interaction triggers a change in a piece of data, the entire set of data for all the pieces of UI flows uni-directionally from the top-level UI to its subcomponents. This strategy helps debugging tremendously since by examining data at a single point we can reason about what when wrong.
RelayJS/GraphQL abstracts the difficult parts of fetching data and caching for UIs into a framework enabling us to simply declare data that we need without having to write AJAX or deal with asynchronous code. This allows us to reduced the server-side to a single API endpoint capable of handling the GraphQL DSL.
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
Rails have long co-existed with Javascript through a variety of ways. As the Javascript ecosystem grows more powerful and complex each day, finding a better way to make Javascript a first-class citizen in the Rails world has become compelling. Rails 5.1 will officially comes with Webpack through the Webpacker gem, but you don't have to wait for that. You can use Webpacker with Rails 4.2+ today. We describe briefly how Javascript existed in the Rails world, and the jump straight into creating a simple Rails/Javascript app from scratch in less 3 minutes.
Tokyo React.js #3: Missing Pages: ReactJS/Flux/GraphQL/RelayJSKhor SoonHin
Missing Pages: ReactJS/Flux/GraphQL/RelayJS. Shed light on assumptions/details glossed over by Facebook docs. Presented at Tokyo React.js Meetup #3 (http://reactjs-meetup.connpass.com/event/26229/)
Japanese version: https://www.slideshare.net/KhorSoonHin/reactjs-3-meetup-ja
Video: https://youtu.be/YFuQlKBXlmA
Described the use of Root Mean Square Logarithmic Error (RMSLE) as cost function in machine learning (ML).
1. What is it?
2. How is it different from RMSE?
3. When to use it?
From Back to Front: Rails To React FamilyKhor SoonHin
ReactJS, Flux, RelayJS, GraphQL, challenges the way we think & code front-end. This presentation explains what they are, how they work together, and how to get them to work on Rails.
In React/Flux, every time a user interaction triggers a change in a piece of data, the entire set of data for all the pieces of UI flows uni-directionally from the top-level UI to its subcomponents. This strategy helps debugging tremendously since by examining data at a single point we can reason about what when wrong.
RelayJS/GraphQL abstracts the difficult parts of fetching data and caching for UIs into a framework enabling us to simply declare data that we need without having to write AJAX or deal with asynchronous code. This allows us to reduced the server-side to a single API endpoint capable of handling the GraphQL DSL.
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
Rails have long co-existed with Javascript through a variety of ways. As the Javascript ecosystem grows more powerful and complex each day, finding a better way to make Javascript a first-class citizen in the Rails world has become compelling. Rails 5.1 will officially comes with Webpack through the Webpacker gem, but you don't have to wait for that. You can use Webpacker with Rails 4.2+ today. We describe briefly how Javascript existed in the Rails world, and the jump straight into creating a simple Rails/Javascript app from scratch in less 3 minutes.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Why Companies Need New Approaches for Faster Time-to-Insight SAP Asia Pacific
An IDC infographic, sponsored by SAP. Volumes and variety of data are continuing to grow. The speed of data usage is also increasing, leading to new user expectations. Given these realities, organizations need to: Sift through data to find meaning, identify risks and opportunities, and identify factors that impact future performance.
What consumers want from marketer is, simply, simplicity – Harvard Business Review reported.
Marketing, however, is not a simple thing. Digitalisation has caused a customer’s journey to no longer be a linear one, as it cuts a multitude of digital and physical brand touch points. Yet understanding the consumer’s intent and where the consumer is likely to be for each of these phases, is crucial for any marketer to be able to deliver a contextual, meaningful marketing effort.
That’s not all. The topic of data – big, small, dark, as well as, analytics remains important. But as marketers, are you harnessing all of this data to help simplify the customer experience?
Join us in an upcoming webinar as we uncover the power of contextual marketing and how SAP Hybris Marketing solution will enable you to deliver that relevant experiences your consumers call, simplicity.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Caliban: Functional GraphQL Library for ScalaPierre Ricadat
Caliban is a library for GraphQL in Scala. It was designed with the goal of reducing boilerplate to a minimum while exposing a purely functional interface. In this talk, we’ll discover how to create a simple GraphQL API from the ground up, then we’ll dig into advanced features such as query optimization and middlewares. Finally, we will take a look at the recently released GraphQL client support.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Why Companies Need New Approaches for Faster Time-to-Insight SAP Asia Pacific
An IDC infographic, sponsored by SAP. Volumes and variety of data are continuing to grow. The speed of data usage is also increasing, leading to new user expectations. Given these realities, organizations need to: Sift through data to find meaning, identify risks and opportunities, and identify factors that impact future performance.
What consumers want from marketer is, simply, simplicity – Harvard Business Review reported.
Marketing, however, is not a simple thing. Digitalisation has caused a customer’s journey to no longer be a linear one, as it cuts a multitude of digital and physical brand touch points. Yet understanding the consumer’s intent and where the consumer is likely to be for each of these phases, is crucial for any marketer to be able to deliver a contextual, meaningful marketing effort.
That’s not all. The topic of data – big, small, dark, as well as, analytics remains important. But as marketers, are you harnessing all of this data to help simplify the customer experience?
Join us in an upcoming webinar as we uncover the power of contextual marketing and how SAP Hybris Marketing solution will enable you to deliver that relevant experiences your consumers call, simplicity.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Caliban: Functional GraphQL Library for ScalaPierre Ricadat
Caliban is a library for GraphQL in Scala. It was designed with the goal of reducing boilerplate to a minimum while exposing a purely functional interface. In this talk, we’ll discover how to create a simple GraphQL API from the ground up, then we’ll dig into advanced features such as query optimization and middlewares. Finally, we will take a look at the recently released GraphQL client support.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.