Guide to Learn Reactjs ContextApi

ReactJS Context API Guide

Introduction

Context API is a mechanism for managing state. It allows you to transfer data across the component tree without having to manually feed data down each level. It prevents props from driling.

Props Drilling:

It occurs when the data of the parent component is supplied to each of the nested child components because the last child requires that data. It can result in needless data transfer, regardless of whether the nested component is required.

Steps to use Context API

1. Create context

React.createContext is used to create context which return two object with Provider and Consumer.

import { createContext } from "react";

export const Context = createContext();

2. Context Provider

Context Provider keeps the data that is used or needed by the childern components. It is placed in parent component.

<Context.Provider value={/* context state data*/}>
      {children}
</Context.Provider>

3. Context Consumer

Context Consumer consumers the data provided by the provider. It basically helps child component to get data or value from provider.

function AppChild() {
      const  { data } = useContext(Context);

      return (
            <section>
                  <p>Context state value: {data}</p>
            </section>
      )
}

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Author: NishChal370
Source code: https://github.com/NishChal370/ReactJS-ContextApi-Guide

#react #typescript #javascript

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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.

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Beginners Guide to Machine Learning on GCP

Introduction to Machine Learning

• Machine Learning is a way to use some set of algorithms to derive predictive analytics from data. It is different than Business Intelligence and Data Analytics in a sense that In BI and Data analytics Businesses make decision based on historical data, but In case of Machine Learning , Businesses predict the future based on the historical data. Example, It’s a difference between what happened to the business vs what will happen to the business.Its like making BI much smarter and scalable so that it can predict future rather than just showing the state of the business.
• **ML is based on Standard algorithms which are used to create use case specific model based on the data **. For example we can build the model to predict delivery time of the food, or we can build the model to predict the Delinquency rate in Finance business , but to build these model algorithm might be similar but the training would be different.Model training requires tones of examples (data).
• Basically you train your standard algorithm with your Input data. So algorithms are always same but trained models are different based on use cases. Your trained model will be as good as your data.

ML, AI , Deep learning ? What is the difference?

ML is type of AI

AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.

Barrier to Entry Has Fallen

AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to

• Increasing availability in data (big data).
• Increase in sophistication in algorithm.
• And availability of hardware and software due to cloud computing.

GCP Machine Learning Spectrum

• For Data scientist and ML experts , TensorFlow on AI platform is more natural choice since they will build their own custom ML models.
• But for the users who are not experts will potentially use Cloud AutoML or Pre-trained ready to go model.
• In case of AutoML we can trained our custom model with Google taking care of much of the operational tasks.
• Pre-trained models are the one which are already trained with tones of data and ready to be used by users to predict on their test data.

Prebuilt ML Models (No ML Expertise Needed)

• As discuss earlier , GCP has lot of Prebuilt models that are ready to use to solve common ML task . Such as image classification, Sentiment analysis.
• Most of the businesses are having many unstructured data sources such as e-mail, logs, web pages, ppt, documents, chat, comments etc.( 90% or more as per various studies)
• Now to process these unstructured data in the form of text, we should use Cloud Natural Language API.
• Similarly For common ML problems in the form of speech, video, vision we should use respective Prebuilt models.

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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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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.

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