Learn to combine RxJs sequences with super intuitive interactive

In this article you’ll find dynamic visual explanations for the most popular RxJS combination operators such as mergeMap, forkJoin and others. The article also describes use cases and termination conditions for each operator.

When working on a sufficiently complex application you usually have data coming from more than one data source. It can be some multiple external data points like Firebase or several UI widgets interacting with a user. Sequence composition is a technique that enables you to create complex queries across multiple data sources by combing relevant streams into one. RxJs provides a variety of operators that can help you do that and in this article we’ll take a look at the most commonly used.

I’ve even become part time animation specialist to design and create most intuitive data flow diagrams that demonstrate the difference between all the operators. However, the diagrams are embedded as an animated GIF so it takes a while for all of them to load. Please be patient.

In the accompanying code I’ll be using lettable operators so if you’re not familiar with them you can do it here. I’ll also be using a custom stream operator that produces a stream of values asynchronously with the first item delivered synchronously upon subscription.

And here is the legend for the type of diagrams I’ll be using throughout this article:

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Merging multiple sequences concurrently#

The first operator we’ll take a look at is merge. This operator combines a number of observables streams and concurrently emits all values from every given input stream. As values from any combined sequence are produced, those values are emitted as part of the resulting sequence. Such process is often referred to as flattening in documentation.

#rxjs #combining-observables #learn #combine #interactive #python

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Learn to combine RxJs sequences with super intuitive interactive

Learn to combine RxJs sequences with super intuitive interactive

In this article you’ll find dynamic visual explanations for the most popular RxJS combination operators such as mergeMap, forkJoin and others. The article also describes use cases and termination conditions for each operator.

When working on a sufficiently complex application you usually have data coming from more than one data source. It can be some multiple external data points like Firebase or several UI widgets interacting with a user. Sequence composition is a technique that enables you to create complex queries across multiple data sources by combing relevant streams into one. RxJs provides a variety of operators that can help you do that and in this article we’ll take a look at the most commonly used.

I’ve even become part time animation specialist to design and create most intuitive data flow diagrams that demonstrate the difference between all the operators. However, the diagrams are embedded as an animated GIF so it takes a while for all of them to load. Please be patient.

In the accompanying code I’ll be using lettable operators so if you’re not familiar with them you can do it here. I’ll also be using a custom stream operator that produces a stream of values asynchronously with the first item delivered synchronously upon subscription.

And here is the legend for the type of diagrams I’ll be using throughout this article:

THIS AD MAKES CONTENT FREE. HIDE

Merging multiple sequences concurrently#

The first operator we’ll take a look at is merge. This operator combines a number of observables streams and concurrently emits all values from every given input stream. As values from any combined sequence are produced, those values are emitted as part of the resulting sequence. Such process is often referred to as flattening in documentation.

#rxjs #combining-observables #learn #combine #interactive #python

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Visit Blog- https://www.xplace.com/article/8743

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Jackson  Crist

Jackson Crist

1617331066

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

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