In this article, we took a brief look at uncertainties in deep learning. Thereafter, we took a more keen look at aleatoric uncertainty and how convolutional autoencoder can help to screen out-of-sample images for classification tasks.
In this blog post, we examine what's new in Ultralytics awesome new model, YOLOv8, take a peak under the hood at the changes to the architecture compared to YOLOv5, and then demo the new model's Python API functionality by testing it to detect on our Basketball dataset.
In this article, we will understand how to use various tools to maximize GPU utilization by finding the right batch size for model training.
In this article, we examine two features of convolutional neural networks, translation equivariance and invariance.
Learn how to customize your diffusion model images with multiple concepts!
In this post, we presented the LSTM subclass and used it to construct a weather forecasting model. We proved its effectiveness as a subgroup of RNNs designed to detect patterns in data sequences, including numerical time series data.
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 tutorial, we cover using sentence embeddings for semantic search using Cohere in a Gradient Notebook
In this followup article, we will be taking a look at another beneficial use of autoencoders. We explored how an autoencoder's encoder can be used as a feature extractor with the extracted features then compared using cosine similarity in order to find similar images.
