Learning Curve Graphs Part 2: The Experiment
In part 1, we discussed the data requirement curse for non-linear machine learning models. We saw that we can use learning curve graphs to estimate the dataset size for target
Learning Curve Graphs Part 1: Countering the Data Requirement Curse
Broadly speaking, most machine learning algorithms fall into one of two categories: linear models or non-linear models. Linear models are easy to interpret, faster to train and deploy
Synthetic Data: What, Why, and How?
It’s no secret that a comprehensive, well-labeled dataset goes a long way towards an effective Machine Learning solution, and while data collection is a large part of…
Is There 20/20 Vision in Computer Vision?
In this post, we discuss how man and machine can work together in the image annotation process to make computer vision training more robust.
Computer Vision Projects Help Support the Fight Against COVID
In this post, Innotescus spotlights several computer vision/ML projects that support the fight against COVID. including a ML model that interprets chest x-rays.
A Complete Guide to an Iterative Process for Data Cleaning
In this blog, we discuss how you can improve the data cleaning process through iteration and prioritization. You can also download a FREE data cleaning rulebook template.
Reducing Data Bias – ML’s Great Challenge
Arguably, the most important challenge facing ML today involves data science and social equity. In this blog, we discuss how bias can be introduced into ML models…
5 Common ML Data Cleaning Problems and How To Solve Them
Though there’s no shortage of data today, most data needs quite a bit of work before it can be leveraged into machine learning solutions. In this blog, we discuss five common…
The Brave 1st Step of Machine Learning: Dealing with Data
Finding data that’s well-suited to train Machine Learning solutions isn’t as easy as it may sound. In this blog we will discuss best practices for managing data along with…
9 Common Steps in Data Annotation
Machine Learning may seem daunting and sound like the science fiction portrayed in The Matrix movies, but in reality, it is merely data, algorithms, and training iterations.