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blog.demofox.org | ||
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thenumb.at
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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... | |
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jaydaigle.net
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| | | | | We continue our exploration of what numbers are, and where mathematicians keep finding weird ones. In the first three parts we extended the natural numbers in two ways: algebraically and analytically. Those approaches gave overlapping but distinct sets of numbers. This week we combine them to get the complex numbers, and see some hints of why the complex numbers are so useful-and so frustrating. | |
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modexp.wordpress.com
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| | | | | Table Of Contents Introduction Affine Transformation Linear Congruential Generator (LCG) Inversive Congruential Generator (ICG) Modular Multiplication Affine Permutation References Appendix Affine Cipher Affine Hash Modular Involutions Lehmer Random Number Generator 1. Introduction In a previous post on masking, shuffling data was suggested as an alternative to encryption or compression that generates high-entropy data. A block... | |
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www.pl-enthusiast.net
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| | | This blog post is the first in my series on secure computationand will be presented in two parts. It briefly introduces some work my collaborators and I published this year, which illustrates a pleasant application of programming languages ideas to ... Continue reading ? | ||
