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This document provides an introduction to reactive programming. It discusses reactive programming as a paradigm oriented around data flows and change propagation. Key concepts covered include treating events as observable arrays, using declarative approaches to describe system behavior over time in response to state changes. The document reviews common array methods like map(), filter(), and reduce() that are used in reactive programming. It also discusses two popular JavaScript libraries, RxJS and BaconJS, that support the reactive programming paradigm. Finally, readers are given a coding challenge to apply these concepts by replacing event handling logic in a project using reactive programming.
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What are algorithms? How can I build a machine learning model? In machine learning, training large models on a massive amount of data usually improves results. Our customers report, however, that training such models and deploying them is either operationally prohibitive or outright impossible for them. At Amazon, we created a collection of machine learning algorithms that scale to any amount of data, including k-means clustering for data segmentation, factorisation machines for recommendations, and time-series forecasting. This talk will discuss those algorithms, understand where and how they can be used, and our design choices.
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Collections forceawakens
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Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Spark and GraphX in the Netflix Recommender System: We at Netflix strive to deliver maximum enjoyment and entertainment to our millions of members across the world. We do so by having great content and by constantly innovating on our product. A key strategy to optimize both is to follow a data-driven method. Data allows us to find optimal approaches to applications such as content buying or our renowned personalization algorithms. But, in order to learn from this data, we need to be smart about the algorithms we use, how we apply them, and how we can scale them to our volume of data (over 50 million members and 5 billion hours streamed over three months). In this talk we describe how Spark and GraphX can be leveraged to address some of our scale challenges. In particular, we share insights and lessons learned on how to run large probabilistic clustering and graph diffusion algorithms on top of GraphX, making it possible to apply them at Netflix scale.
A full Machine learning pipeline in Scikit-learn vs in scala-Spark: pros and ...
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The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn? At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting; which would you use in production?
The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn?
At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting -- in several different frameworks. We'll show what it's like to work with native Spark.ml, and compare it to scikit-learn along several dimensions: ease of use, productivity, feature set, and performance.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Dev Games!
- 1. DEV GAMES! Cultivate community, have fun exploring new technology
- 2. Jan 12 2016: Reactive Crash Course! • What is Reactive Programming? – A programming paradigm oriented around data flows and the propagation of change – Wikipedia – Dealing with events as observable arrays (event streams) – Describe the behavior of your system, particularly as it relates to state changes over time, declaratively
- 3. Declarative vs Imperative Approach • Imperative: you describe exactly how you want something to happen • Declarative: you describe what you want to happen, but let the compiler take care of how
- 4. How do you describe the behavior of events Declaratively?
- 5. Combine Observable Pattern and common Array Methods • In the observable pattern you have observables (broadcasters) and observers (subscribers). It is a way for different objects in a system to communicate • Common array methods such as filter() map() reduce() allow you to do the equivalent of iterating an array and performing some transformation
- 6. Array Method Review map(), filter(), reduce()
- 7. Array.map() • map() creates a new array with the results of calling a function on every element in this array.
- 8. Array.filter() • filter() tests each element of an array and returns result array with items for which the test evaluated to true
- 9. Array.reduce() • reduce() applies a function against an accumulator and each value of the array (from left-to-right) to reduce it to a single value. Good for addition.
- 10. Putting it all together • Two most popular javascript libraries are: – RxJS https://github.com/Reactive-Extensions/RxJS – BaconJS https://baconjs.github.io/
- 11. CODE Challenge! • Use Reactive programming to replace the event handling logic in a mini project of your choice (could be in existing project, or project from github etc. , or you can start from scratch) • You have one hour • Option to clone this repo and reverse engineer or experiment git@bitbucket.org:adamhaley/dev-games.git
- 12. Learn More : The Introduction to reactive programming you've been missing: https://gist.github.com/staltz/868e7e9bc2a7b8c1f754 Video Lesson Series on egghead.io: https://egghead.io/series/introduction-to-reactive- programming Angular / Reactive Introduction: https://medium.com/google-developer-experts/angular- introduction-to-reactive-extensions-rxjs-a86a7430a61f