Software Alternatives & Reviews

AETROS VS Apple Machine Learning Journal

Compare AETROS VS Apple Machine Learning Journal and see what are their differences

AETROS logo AETROS

Create, train and monitor deep neural networks

Apple Machine Learning Journal logo Apple Machine Learning Journal

A blog written by Apple engineers
  • AETROS Landing page
    Landing page //
    2023-07-18
  • Apple Machine Learning Journal Landing page
    Landing page //
    2022-12-13

AETROS

Categories
  • AI
  • Games
  • Action
  • Developer Tools
Website deepkit.ai

Apple Machine Learning Journal

Categories
  • AI
  • Developer Tools
  • Data Science And Machine Learning
  • APIs
Website machinelearning.apple.com

Category Popularity

0-100% (relative to AETROS and Apple Machine Learning Journal)
AI
24 24%
76% 76
Developer Tools
22 22%
78% 78
Games
100 100%
0% 0
Data Science And Machine Learning

User comments

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Social recommendations and mentions

Based on our record, Apple Machine Learning Journal should be more popular than AETROS. It has been mentiond 6 times since March 2021. 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.

AETROS mentions (1)

  • Introducing Deepkit ORM, a high performance ORM for TypeScript
    Deepkit ORM is one of a whole collection of high performance libraries written in the last years for my need in developing complex isomorphic TypeScript applications (like for example https://deepkit.ai). Since we approach the beta version I'd like to introduce you to one of its flagship libraries, the ORM, and collect feedback. So, if you are interested, please keep reading and drop me a comment about your thoughts! Source: almost 3 years ago

Apple Machine Learning Journal mentions (6)

  • Does anyone else suspect that the official iOS ChatGPT app might be conducting some local inference / edge-computing? [Discussion]
    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: 11 months ago
  • Which papers should I implement or which Projects should I do to get an entry level job as a Computer vision engineer at MAANG ?
    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
  • Apple’s secrecy created engineer burnout
    They have something for ML: https://machinelearning.apple.com. - Source: Hacker News / almost 2 years ago
  • [D] Is anyone working on open-sourcing Dall-E 2?
    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: almost 2 years ago
  • How does Apple achieve both secrecy and quality for a release?
    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
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What are some alternatives?

When comparing AETROS and Apple Machine Learning Journal, you can also consider the following products

Colornet - Neural Network to colorize grayscale images

Amazon Machine Learning - Machine learning made easy for developers of any skill level

Quick Draw Game - Can a neural network learn to recognize doodles?

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

TensorFlow Lite - Low-latency inference of on-device ML models

Lobe - Visual tool for building custom deep learning models