In this article, we examine typical computer vision analysis techniques in comparison with the modern CLIP (Contrastive Language-Image Pre-Training) model.
We compare the performance of using different techniques for handling Missing At Random datasets in building predictive models. We also examine how these techniques affect the predictive performance of machine learning models.
When it comes to image synthesis algorithms, we need a method to quantify the differences between generated images and real images in a way that corresponds with human judgment. In this article, we highlight some of these metrics that are commonly used in the field today.
In this article, we saw how to use various tools to maximize GPU utilization by finding the right batch size for model training in Gradient Notebooks.
In this article, we examine the game theory based approach to explaining outputs of machine learning models: Shapely Additive exPlanations or SHAP. We then demo the technology using sample images in a Gradient Notebook.
In this article, we talk about what Dense Passage Retrieval is, how it works, and its uses. We also show how to implement it using the Simple Transformers python library in a Gradient Notebook.
In this article, we examine the processes of implementing training, undergoing validation, and obtaining accuracy metrics - theoretically explained at a high level. We then demonstrate them by combining all three processes in a class, and using them to train a convolutional neural network.
In this article, we explore what dimensions imply in a convolutional neural network context. We show how to create a custom convnet as a baseline model, and then proceed to create new versions of it, with increased width, increased depth and the last one with both increased depth and width.
This tutorial provides step-by-step instructions on how to handle class imbalances in image datasets for training computer vision models.
