Sadie  Cassin

Sadie Cassin

1616571282

Apollo Client vs. Redux: Learnings and Experience

The ever changing world of front end development continues to evolve and introduce many different libraries to solve your problems. As GraphQL continues to grow in popularity, the tools and libraries surrounding it change also. Recently I helped write a new application that interacted solely with a GraphQL backend, and used Apollo Client (AC) to interact with instead of Redux in our React frontend application. Having previously written many javascript apps with Redux, here are some takeaways from my experience.

Disclaimer: Comparing the two libraries assumes you are working with a GraphQL backend. Apollo Client doesn’t work with other types of backends and isn’t a viable alternative to Redux in that case.

#graphql #redux #apollo-client #javascript #apollo

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Apollo Client vs. Redux: Learnings and Experience
Sadie  Cassin

Sadie Cassin

1616571282

Apollo Client vs. Redux: Learnings and Experience

The ever changing world of front end development continues to evolve and introduce many different libraries to solve your problems. As GraphQL continues to grow in popularity, the tools and libraries surrounding it change also. Recently I helped write a new application that interacted solely with a GraphQL backend, and used Apollo Client (AC) to interact with instead of Redux in our React frontend application. Having previously written many javascript apps with Redux, here are some takeaways from my experience.

Disclaimer: Comparing the two libraries assumes you are working with a GraphQL backend. Apollo Client doesn’t work with other types of backends and isn’t a viable alternative to Redux in that case.

#graphql #redux #apollo-client #javascript #apollo

Alfredo  Sipes

Alfredo Sipes

1617270679

Top 10 Fun Machine Learning Experiments By Google Released in 2020

Experiments with Google is an exciting website where Google developers, as well as others around the globe, creates intuitive experiments based on machine learning and other techniques. The experiments are the projects that push the boundaries of technology, art, design and more. Currently, the website includes more than 1500 experiments.

Here, we have curated a list of ten such fun machine learning experiments that are released in 2020.

#google experiments #google machine learning #machine learning #machine learning algorithms #machine learning experiments

Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Kennith  Kuhic

Kennith Kuhic

1620778500

Machine Learning Vs Deep Learning: Difference Between Machine Learning and Deep Learning

Machine learning and Deep learning both are the buzzwords in the tech industry. Machine learning and deep learning both are the subdivision of artificial intelligence technology. If we further breakdown, deep learning is a subdivision of machine learning technology.

If you are familiar with the basics of machine learning and deep learning, it is excellent news!

However, if you are new to the AI field, then you must be confused. What is the difference between machine learning and deep learning?

There is nothing to worry about. This article will explain the differences in easy to understand language.

What is Machine Learning?

Machine learning is a branch of technology that studies computer algorithms. These algorithms allow the system to learn from data or improve by itself through experience. Machine learning algorithms make predictions or decisions without being explicitly programmed.

#artificial intelligence #comparison #deep learning #machine learning #machine learning vs deep learning

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