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Machine learning is a type of artificial intelligence that allows models to learn from data by identifying patterns in existing datasets and using them to make predictions on unseen or unknown data. Model Generalization is a crucial trait that must be present in ML models trained and deployed in production....
Guest Contributor
Machine Learning (ML) is a field of study that focuses on developing algorithms to learn automatically from data, making predictions and inferring patterns without being explicitly told how to do it. It aims to create systems that automatically improve with experience and data. This can be achieved through supervised learning,...
In machine learning, the bias-variance trade-off is a fundamental concept affecting the performance of any predictive model. It refers to the delicate balance between bias error and variance error of a model, as it is impossible to simultaneously minimize both. Striking the right balance is crucial for achieving optimal model...
This guide is the third and final part of three guides about Support Vector Machines (SVMs). In this guide, we will keep working with the forged bank notes use case, have a quick recap about the general idea behind SVMs, understand what is the kernel trick, and implement different types...
Cássia Sampaio
This guide is the second part of three guides about Support Vector Machines (SVMs). In this guide, we will keep working on the forged bank notes use case, understand what SVM parameters are already being set by Scikit-Learn, what are C and Gamma hyperparameters, and how to tune them using...
This guide is the first part of three guides about Support Vector Machines (SVMs). In this series, we will work on a forged bank notes use case, learn about the simple SVM, then about SVM hyperparameters and, finally, learn a concept called the kernel trick and explore other types of...
You are part of a project that will use deep learning to try to identify what is in images - such as cars, ducks, mountains, sky, trees, etc. In this project, two things are important - the first one, is that the deep learning model trains quickly, with efficiency (because...
Neural Radiance Fields, colloquially known as NeRFs have struck the world by storm in 2020, released alongside the paper "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis", and are still the cornerstone of high quality synthesis of novel views, given sparse images and camera positions. Since...
David Landup
Byte
Tree-based models have become a popular choice for Machine Learning, not only due to their results, and the need for fewer transformations when working with data (due to robustness to input and scale invariance), but also because there is a way to take a peek inside of them to see...
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