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inverseprobability.com | ||
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www.v7labs.com
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| | | | | Training data is key to success in machine learning. Learn how to select & label data for different types of tasks, such as image classification or object detection. | |
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www.analyticsvidhya.com
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| | | | | Ball tracking system is a part of DRS. Here, learn how to build a ball tracking system for cricket using object tracking, deep learning and Python. | |
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programminghistorian.org
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| | | | | [AI summary] This article provides an introduction to deep learning for image classification, focusing on its application in the humanities, particularly for analyzing historical newspaper advertisements. The authors discuss the use of transfer learning with pre-trained models like ResNet to improve classification accuracy, as demonstrated through experiments comparing models with and without transfer learning. They also highlight the importance of data preparation, annotation, and the challenges of working with historical data. The article includes practical examples, such as the Newspaper Navigator Dataset, and references to relevant literature and tools like fastai. It concludes with a call for continued support for open-access resources in digital humani... | |
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jaketae.github.io
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| | | In this short post, we will take a look at variational lower bound, also referred to as the evidence lower bound or ELBO for short. While I have referenced ELBO in a previous blog post on VAEs, the proofs and formulations presented in the post seems somewhat overly convoluted in retrospect. One might consider this a gentler, more refined recap on the topic. For the remainder of this post, I will use the terms "variational lower bound" and "ELBO" interchangeably to refer to the same concept. I was heavily inspired by Hugo Larochelle's excellent lecture on deep belief networks. | ||
