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 liorsinai.github.io
MicroGrad.jl: Part 1 ChainRules - Lior Sinai
| | windowsontheory.org
Yet another backpropagation tutorial - Windows On Theory
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| | (Updated and expanded 12/17/2021) I am teaching deep learning this week in Harvard's CS 182 (Artificial Intelligence) course. As I'm preparing the back-propagation lecture, Preetum Nakkiran told me about Andrej Karpathy's awesome micrograd package which implements automatic differentiation for scalar variables in very few lines of code. I couldn't resist using this to show how...
| | jingnanshi.com
Automatic Differentiation: Forward and Reverse
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| | Tutorial on automatic differentiation
| | matbesancon.xyz
Differentiating the discrete: Automatic Differentiation meets Integer Optimization |
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| | What can automated gradient computations bring to mathematical optimizers, what does it take to compute?
| | blog.adnansiddiqi.me
Test-Driven Development: a Cost-Effective Approach to Developing Software | Adnan's Random bytes
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| The main goal of software developers is to create high-quality products with the required functionality. At the same time, this product creation shouldn't cost a fortune. One of the effective methods used to speed up the process of creating software and reduce its cost is test-driven development, which offers a pool of benefits to both