Backpropagation is not magic and it is not new. It is the chain rule from first-year calculus, applied mechanically to a computational graph in reverse. This is what actually happens when a network learns: the two passes, a worked example in NumPy, why it runs backward instead of forward, where the gradients vanish and explode, and what the optimizer does with the numbers.
Optimization
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Backpropagation: How Neural Networks Actually Learn -
Calculus for the Person Who Forgot A working engineer's guide to the calculus that actually earns its keep: derivatives as sensitivity, gradients for optimization, the chain rule as backpropagation, and integrals as accumulation. Plus the curriculum you can safely forget.
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Why Your Homelab Burns 300W Doing Nothing Where 300W of idle homelab power actually goes, and how to claw most of it back: C-states, ASPM, powertop, HDD spindown, BIOS settings, consolidation onto mini-PCs, and scheduled wake-on-LAN — with the savings math worked out at real electricity prices.
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Web Performance Engineering Making pages fast, measured properly — Core Web Vitals (LCP, INP, CLS) and what actually moves them, the critical rendering path, resource hints, lazy loading and code-splitting, caching and CDN strategy, image and font optimization, and measuring with lab tools and real-user monitoring. The SRE mindset applied to the frontend.
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Profiling Applications: Finding Bottlenecks in Go, Python, and Node.js Stop guessing why your application is slow. This guide covers systematic profiling tools and techniques for Go (pprof), Python (cProfile, py-spy, line_profiler), and Node.js (clinic.js, V8 profiler) so you can find the actual bottleneck and fix it with confidence.
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Caching Strategies for Performance How to implement caching effectively at different layers of your application.