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 windowsontheory.org
Yet another backpropagation tutorial - Windows On Theory
| | marcospereira.me
Backpropagation From Scratch-Marcos Pereira
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| | In this post we summarize the math behind deep learning and implement a simple network that achieves 85% accuracy classifying digits from the MNIST dataset.
| | thenumb.at
Differentiable Programming from Scratch
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| | [AI summary] This text provides a comprehensive overview of differentiable programming, focusing on its application in machine learning and image processing. It explains the fundamentals of automatic differentiation, including forward and backward passes, and demonstrates how to implement these concepts in a custom framework. The text also discusses higher-order differentiation and its implementation in frameworks like JAX and PyTorch. A practical example is given using differentiable programming to de-blur an image, showcasing how optimization techniques like gradient descent can be applied to solve real-world problems. The text emphasizes the importance of differentiable programming in enabling efficient and flexible computation for various domains, includ...
| | newvick.com
Understanding Backpropagation Newvick's blog
2.0 parsecs away

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| | I've been working my way through Andrej Karpathy's 'spelled-out intro to backpropagation', and this post is my recap of how backpropagation works.
| | comsci.blog
Step-by-Step Guide to Image Classification with Vision Transformers (ViT) | ML and robotics notes
12.9 parsecs away

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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.