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Based on our record, Google Images seems to be a lot more popular than Apple Machine Learning Journal. While we know about 625 links to Google Images, 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.
Go to Google Images then choose Search by Image (middle button) and paste in an image link. You get a few similar images, one says Dubai, which at least gives you the city. Then go to Google Maps, type in McCafe (there are a few) and start looking at Street Views for each location until you find it. Source: 7 months ago
How can I check whether my design is unique or not? You can check by the following two methods: 1- Google Reverse Search Https://images.google.com/ 2- Tineye (https://Tineye.com) Visit any above-mentioned site and then simply submit your purchased png file to check it for uniqueness. In case of any matching, you can make a complaint by visiting our 'contact us' page. Source: 7 months ago
Go to https://images.google.com/ and then type in the search term "schematics". Refine your quest by using additional search terms like "arduino" or "5 volts" or "beginner circuits", "simple circuits", "breadboard experiements" etc. Source: 7 months ago
Everyone’s stb scenes are different. you’ll have to find that scene on one of the many jj help sites such as: regis (make sure to use pound sound / hashtag in between listed items instead of spaces), thatsleuthlife (make sure to use semicolon between listed items instead of space), find.june (upload the grayed {locked} or colored {unlocked} mini picture on the bottom of the screen where you view the steps),... Source: 7 months ago
In addition to no 2, you may also upload the image to https://images.google.com. Source: 12 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
TinEye - Reverse Image Search to help find an image's source, duplicates or altered versions.
Amazon Machine Learning - Machine learning made easy for developers of any skill level
SauceNAO - SauceNAO is a reverse image search engine.
Machine Learning Playground - Breathtaking visuals for learning ML techniques.
Yandex.Images - search for images on the internet, search by image
Lobe - Visual tool for building custom deep learning models