Machine Learning
How machines learn from examples: models, loss, gradient descent and neural networks, built up from scratch.
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Categories
11 topics · 29 posts
- HTML19 postsThe structure of the web: elements, semantics, and the DOM.
- CSS3 postsStyling, layout, and design systems on the web.
- General3 postsDeveloper tools, toolkits and libraries worth knowing, from image converters to React icon sets.
- Web3 postsHow the browser itself works: storage, caching and the APIs underneath every page.
- History1 postThe people and ideas behind computing, from Turing to the first web page.
- AndroidsoonJetpack Compose and Kotlin. Building for the phone in your pocket.
- BackendsoonDatabases, APIs, and everything behind the request.
- JavaScriptsoonThe language itself and the runtime around it.
- KotlinsoonThe Kotlin language itself, from val and var to coroutines, on any platform.
- Machine LearningsoonHow machines learn from examples: models, loss, gradient descent and neural networks, built up from scratch.
- Pack CamosoonGuides, how-tos and behind-the-scenes notes on Pack Camo, the free browser toolkit for PDFs, images and files that never uploads them.
Learning paths
- Learning HTML19 stepsFrom basic elements to semantic markup. How HTML gives structure and meaning to the web.
- Learning CSS3 stepsFrom your first rule to modern layout and motion. Every post has live demos you can poke at. Read in order, or dip into a single part.