Based on our record, Learn X in Y minutes seems to be a lot more popular than Apple Machine Learning Journal. While we know about 146 links to Learn X in Y minutes, we've tracked only 6 mentions of Apple Machine Learning Journal. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
> Sure, maybe for some esoteric edge cases, but 5 mins on https://learnxinyminutes.com/ should get you 80% of the way there, and an afternoon looking at big projects or guidelines/examples should you another 18% of the way. Not for C++, and even for other languages, it's not the language that's hard, it's the idioms. Python written by experts can be well-nigh incomprehensible (you can save typing out... - Source: Hacker News / about 2 months ago
> Learning a new language shouldn't be difficult. Programmers are expected to familiarize themselves with new tech. I wish any large company agreed with this. I've worked for a company that on boarded every single new engineer to a very niche language (F#) in a few days. Also, everybody I worked with there was amazing. Probably because of that kind of mindset. Meanwhile google tiptoes around teams adopting kotlin... - Source: Hacker News / about 2 months ago
When I want to get a quick feel for a language I've never heard of, I usually look for the Learn X in Y Minutes[0] page for it. Shen doesn't have one. Perhaps the author and/or poster should remedy that? [0] https://learnxinyminutes.com/. - Source: Hacker News / 3 months ago
Learn x in y minutes: Concise tutorials to learn various programming languages and tools quickly. - Source: dev.to / 3 months ago
StackOverflow's making their own competing LLM for all this stuff. IMO, one of the biggest problems with the way people use LLMs right now, is that they're being treated as a single oracle: to know Java, it must be trained on examples of Java. It would be much better if their language comprehension abilities were kept separated from their knowledge (and there are development efforts in this direction), so in this... - Source: Hacker News / 4 months ago
For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: about 1 year ago
We even host annual poster sessions of those PhD intern’s work while at our company, and it’ll give you an idea of the caliber of work. It may not be as great as Nvidia, Stryker, Waymo, or Tesla (which are not part of MAANG but I believe are far more ahead in CV), but it’s worth of considering. Source: about 1 year ago
They have something for ML: https://machinelearning.apple.com. - Source: Hacker News / about 2 years ago
They're more subtle about it, I think. https://machinelearning.apple.com/ Some of the papers are pretty good. I don't disagree with your sentiment in aggregate, though. Source: about 2 years ago
Siri is not where it needs to be because Apple refuses to mine user data to enrich it. They also are very hesitant to allow researchers to publish their breakthroughs which makes recruitment very hard. Although this is changing https://machinelearning.apple.com/. - Source: Hacker News / about 2 years ago
Exercism.io - Download and solve practice problems in over 30 different languages.
Amazon Machine Learning - Machine learning made easy for developers of any skill level
SyntaxDB - Easily look up programming syntax for multiple languages
Machine Learning Playground - Breathtaking visuals for learning ML techniques.
GitHub Visualizer - Enter user/repo and see the project visually
Lobe - Visual tool for building custom deep learning models