In the age of ubiquitous mobile apps, ensuring their quality and functionality across diverse devices and platforms is crucial. Manual testing, while essential, can be time-consuming and prone to human error.expand_more Enter mobile automation testing frameworks, your allies in streamlining the process and delivering exceptional user experiences. However, choosing the right framework amidst a...
Kotlin Best Practices: Elevate Your Code
Introduction: Kotlin, the versatile and powerful programming language, has taken the development world by storm. Its concise syntax, seamless interoperability with Java, and robust features have made it a favorite among developers. In this article, we will delve into the top 10 Kotlin best practices that will not only elevate your coding skills but also boost your app’s performance and...
The Top 5 Most In-Demand Programming Languages to Learn for a Bright Future
Introduction: When Computers Were Human by NASA’s Marshall Space Flight Center is licensed under CC-BY-NC 2.0 In the fast-paced world of technology, staying ahead of the curve is crucial for aspiring programmers. As the demand for software developers continues to surge, knowing which programming languages are most sought-after can give you a competitive edge in the job market. In this...
Advanced Keras – Custom loss functions
When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often...
Latent Dirichlet Allocation (LDA) and Topic ModelLing in Python
Topic modelling is a type of statistical modelling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of a topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modelled as Dirichlet distributions. Here, we are...