Software Alternatives & Reviews

GPT3 Crush VS Apple Machine Learning Journal

Compare GPT3 Crush VS Apple Machine Learning Journal and see what are their differences

GPT3 Crush logo GPT3 Crush

Curated list of OpenAI's GPT3 demos

Apple Machine Learning Journal logo Apple Machine Learning Journal

A blog written by Apple engineers
  • GPT3 Crush Landing page
    Landing page //
    2021-08-10
  • Apple Machine Learning Journal Landing page
    Landing page //
    2022-12-13

Category Popularity

0-100% (relative to GPT3 Crush and Apple Machine Learning Journal)
AI
42 42%
58% 58
Developer Tools
33 33%
67% 67
Productivity
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 GPT3 Crush. 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.

GPT3 Crush mentions (1)

  • I had an AI write an article on Life Hacks
    Link to demos / apps powered by GPT-3: https://gptcrush.com/resources/. Source: over 2 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: 12 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 / about 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: about 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 GPT3 Crush and Apple Machine Learning Journal, you can also consider the following products

GPT-3 Demo - A showcase of 60+ GPT-3 resources, examples, and use cases

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

Copysmith - GPT-3 powered content marketing that feels like magic

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

OpenAI - GPT-3 access without the wait

Lobe - Visual tool for building custom deep learning models