A new Google Research study has proposed a unified, efficient and modular approach for implicit differentiation of optimization problems that combines the benefits of implicit differentiation and automatic differentiation (autodiff). The researchers say solvers equipped with implicit differentiation set up by the proposed framework can make the autodiff process more efficient for end-users.Read More
In this article, we’ll talk about some often-misunderstood development principles that will guide you to developing more resilient, production-ready development pipelines using CI/CD tools. Then, we’ll make it concrete with a tutorial about how to set up your own pipeline using Buddy.Read More
Creating a machine learning model is a difficult task because we need to make a model which works best for our data and we can optimize for better performance and accuracy. Generally making a machine learning model is easy but find out the best parameters and optimizing is a time taking process.Read More
One of the most frequent steps on a machine learning pipeline is splitting data into training and validation sets. It is one of the necessary skills all practitioners must master before tackling any problem. The splitting process requires a random shuffle of the data followed by a partition using a preset threshold. On classification variants, you may want to use stratification to ensure the same distribution of classes on both sets. When handling time series data, you might want to skip shuffling and keep the earliest observations on the training set.Read More
Coding a solution for the 8-queens puzzle with Genetic Search Algorithms, AI algorithms that mimic how our genes are passed and expressed. James Watson and Francis Crick first published their groundbreaking study on the double-helix form of deoxyribonucleic acid (DNA) and how it expresses our genes, which up until then, were mere abstractions lacking a concrete, biological, definition.Read More
This is my go-to method. The main difference between the CatBoost, XGBoost, and H2O versions relate to the ML parameters available for tuning. All functions listed in this blog have working examples in the GitHub README, the R help files (which can be opened in your R session) or the package reference manual.Read More
In this article, I want to take an in-depth look at regularization.Read More
The global pandemic has persuaded a number of manufacturers to pursue an AI-driven transformation of their operations, according to studies by Cap Gemini and the Boston Consulting Group. What has been noticed is the way in which they are combining human experience and insight with AI tools to find ways to differentiate themselves from their competitors.Read More
The model should conform to these assumptions to produce a best Linear Regression fit to the data.Read More
We demonstrate an efficient method for simultaneously tuning discrete and continuous hyperparameters for machine learning models using policy gradients.Read More
Tabu Search is a popular algorithm used to optimize a multi-parameter model that can yield exceptional results. Although the implementation is not trivial and requires tuning, it is capable of solving a wide variety of problems once it is created.Read More
Your CRM has messy data. Sales teams are getting irrelevant prospects. Marketing is making embarrassing mistakes. Reports keep giving incorrect insights. You introduce more processes. You invest. More marketing budget allocated. More resources are added.
Sounds all too familiar? You’re not alone.
The food industry is one industry where robots will have a profound impact. In many ways, they are already starting to do so. As such, here are 7 instances where robots are already being used in the food industry, from manufacturing all the way to front-end customer service.Read More