Machine Learning

Recently I took an interest in identifying the language of a snippet of text. Let’s say I get a text message. It says “Bienvenue!” If I have seen this word, I’d already have a mental map of the word to its language and meaning. The problem is that people typically understand one language. Maybe two or three languages if you’re gifted. If I were to build a computer to do this, … [Read more...] about A Exploration of Short Text Language Identification

Let’s say there is a population of dogs living in Puppy City; a new job as a customer support representative at a company has opened up, and the company wants to use machine learning to making a hiring decision. By using machine learning, the company says, they’ll be able to reduce bias in the hiring manager’s decision making process.But there’s a problem — Puppy City … [Read more...] about Can Machine Learning Ever Be “Fair” — and What Does That Even Mean?

K-means is a clustering algorithm that belongs to supervised learning. You might hear of K-nearest neighbors. Both words contain the same letter “K,” such that you might think they are a similar algorithm or have something in common. However, they are different algorithms.First of all, K-nearest neighbors is a supervised learning algorithm. This algorithm is mainly used for a … [Read more...] about K-Means — Machine Learning (Unsupervised Learning)

There are several different definitions of Digital Twins or Clones and many use them interchangeably with terms such as Industry 4.0 or the Industrial Internet of Things (IIOT). Fundamentally Digital Twins are digital representations of a physical asset, process or product and behave similarly to the object it represents. The concept of Digital Clones has been around for a … [Read more...] about Digital Twin in Manufacturing

Paul Ford:Everyone should stay really focused on incrementally using machine learning and AI to solve really basic problems. You thread enough of those together, it looks transformation.James Kotecki:This is Machine Meets World, Infinia ML’s ongoing conversation about artificial intelligence. I am James Kotecki, and my guest today is Paul Ford, the CEO of Traffk. Welcome to the … [Read more...] about Traffk CEO Paul Ford: Incremental AI is Transformational

You can assess your MLOps maturity by considering the level of automation and reproducibility that you have in your AI projects. One approach proposed in this article is to define three levels:MLOps level 0 is a manual process for AI initiatives.MLOps level 1 brings ML pipeline automation.MLOps level 2 supports a full CI/CD pipeline for all ML activities.Now how can you … [Read more...] about Google Cloud services for MLOps

A Naive Bayes classifier is a supervised learning classifier that uses Bayes’ theorem to build the modelA classifier solves the problem of identifying sub-populations of individuals with certain features in a larger set, with the possible use of a subset of individuals known as a priori (a training set).The underlying principle of a Bayesian classifier is that some individuals … [Read more...] about Building a Naive Bayes classifier

In a complete project, the steps to be performed before arriving at the extraction of the features are many, the main ones can be divided into four macro phases, each with criticalities to be recognized and solved in order to obtain a performing machine learning model.Dataset AnalysisPreprocessingFeatures Extraction & Features SelectionNormalizationOur goal is to try to … [Read more...] about How to extract Features from Signals

A developer’s worth often rests in their ability to keep up with new technologies and learn new programming languages on the fly.As I mentioned above, it can feel like you’re running on a never-ending hamster wheel of new technologies to remain “relevant.” This phenomenon is what drives people into tech… and what drives people away from tech.In school, you have a pretty good … [Read more...] about 3 Roadblocks Any New Software Developer Can Encounter

Recall that in a common scenario where we perform supervised learning, the goal is to understand and formulate the relationship between the observable data attributes X and the target values Y. When there is little prior knowledge about how X is connected to Y, or it is difficult to represent such knowledge (consider specifying the pixels in an image and the visual concept of … [Read more...] about Neural Network — multilayer perceptron