Article
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". On one end, it can be used to build autonomous systems that navigate agents through environments - be it robots performing tasks or self-driving cars, but...
David Landup
There are plenty of guides explaining how transformers work, and for building an intuition on a key element of them - token and position embedding. Positionally embedding tokens allowed transformers to represent non-rigid relationships between tokens (usually, words), which is much better at modelling our context-driven speech in language modelling....
K-means clustering is an unsupervised learning algorithm that groups data based on each point euclidean distance to a central point called centroid. The centroids are defined by the means of all points that are in the same cluster. The algorithm first chooses random points as centroids and then iterates adjusting...
Cássia Sampaio
K-Means clustering is one of the most widely used unsupervised machine learning algorithms that form clusters of data based on the similarity between data instances. In this guide, we will first take a look at a simple example to understand how the K-Means algorithm works before implementing it using Scikit-Learn....
K-Means is one of the most popular clustering algorithms. By having central points to a cluster, it groups other points based on their distance to that central point. A downside of K-Means is having to choose the number of clusters, K, prior to running the algorithm that groups points. If...
Byte
Models are more often than not trained to be deployed to production and to give meaningful predictions for new input. To move them outside of your training environment - you'll want to save a trained model and load it in a different one. XGBoost is a great, flexible and blazingly...
Computer Vision models have come a long way - and you can leverage existing models, pre-trained on large corpora of data, and just plug them into your prediction pipeline. While fine-tuning a network is the best way to go - importing an existing model and running predictions from the get-go...
Agglomerative Hierarchical Clustering is an unsupervised learning algorithm that links data points based on distance to form a cluster, and then links those already clustered points into another cluster, creating a structure of clusters with subclusters. It is easily implemented using Scikit-Learn which already has single, average, complete and ward...
Regression is a technique in statistics and machine learning, in which the value of an independent variable is predicted by its relationship with other variables. Frameworks like Scikit-Learn make it easier than ever to perform regression with a wide variety of models - one of the strongest ones being built...
© 2013-2023 Stack Abuse. All rights reserved.
