Domino Data Science Blog

Fitting complex neural network models is a computationally heavy process, which requires access to large amounts of data. In this article we introduce transfer learning - a method for leveraging pre-trained model, which speeds up the fitting and removes the need of processing large amounts of data. We provide guidance on transfer learning for NLP and computer vision use-cases and show simple implementations of transfer learning via Hugging Face.

In a previous blog post we talked about the foundations of Computer vision, the history and capabilities of the OpenCV framework, and how to make your first steps in accessing and visualising images with Python and OpenCV. Here we dive deeper into using OpenCV and DNNs for feature extraction and image classification.

In this article we talk about the foundations of Computer vision, the history and capabilities of the OpenCV framework, and how to make your first steps in accessing and visualising images with Python and OpenCV.

In this article, we will introduce and implement k-nearest neighbours (KNN) as one of the supervised machine learning algorithms. KNN is utilised to solve classification and regression problems. We will provide sufficient background and demonstrate the utility of KNN in solving a classification problem in Python using a freely available dataset.