One of the many complaints about the GraphQL ecosystem is that there is a lot of indirection around what packages to use to create a GraphQL application. Even after selecting one, there is a lot of boilerplate code that one must create to make a GraphQL server work. While that...
Natural Language Processing has gained a lot of popularity owing to an exponential increase in unstructured data generation. Here are some fun data to give you context – The world sees 682 million tweets per day There exists 1.2 million new data producing social media users each day Up to...
When writing unit-tests for JavaScript modules, we often encounter a dilemma wherein the module has some private functions that have not been exported. Testing a function that has been exported is easy since it can be imported in the unit testing framework, and the functionality can be tested. But how...
If you have tried developing your own dropdown, modal, or popover in React, you would have come across this. “How do I detect a click outside my react component so that I can close it?” Detecting click outside component is luckily is not that difficult. This post will use react...
If you are reading this post right now, chances are high that you are not looking for the number of islands in the Pacific Ocean. But you are planning to appear for a programming interview in the future and/or you have appeared for a coding interview in the past. I...
In this article, we will explore recurrent neural networks. For all the readers who are not versed with the concept of neural networks, I will recommend that you go through one of our previous articles and get a basic understanding of how a neural network works. Disclaimer – The concepts...
Protocol buffers or Protobuf, is a binary format that was created by Google to serialize structured data that is transferred among services. Before we get into what protocol buffers are, let us first look into JSON. JSON clearly has great advantages when it comes to being a data interchange format...
Data distributions lie at the heart of all the machine learning algorithms and data science techniques. A machine learning algorithm is only as good as the data it gets. Hence, it is important to fully understand the data and data distributions before we build our models. Consequently, explore and understand...
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