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wtfleming.github.io | ||
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comsci.blog
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| | | | | In this blog post, we will learn about vision transformers (ViT), and implement an MNIST classifier with it. We will go step-by-step and understand every part of the vision transformers clearly, and you will see the motivations of the authors of the original paper in some of the parts of the architecture. | |
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blog.paperspace.com
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| | | | | Follow this tutorial to learn what attention in deep learning is, and why attention is so important in image classification tasks. We then follow up with a demo on implementing attention from scratch with VGG. | |
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www.jeremymorgan.com
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| | | | | Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits. | |
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transformer-circuits.pub
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| | | [AI summary] The text discusses the interpretability of features in a machine learning model, focusing on how features like Arabic, base64, and Hebrew are used in interpretable ways. It explores the extent to which these features explain the model's behavior, noting that features with higher activations are more interpretable. The text also addresses the limitations of current methods, such as the computational cost of simulating features and the potential for dataset correlations to influence feature interpretations. Finally, it concludes that the model's learning process creates a richer structure in its activations than the dataset alone, suggesting that feature-based interpretations provide meaningful insights into the model's behavior. | ||
