Fritz AI Heartbeat
heartbeat.fritz.ai · Company blog · on HN since 2017
Fritz AI's Heartbeat publication covers deep learning, mobile machine learning, and Apple hardware, written by a mix of contributors.
What HN saysHN links it for explainers on speech synthesis and model size. One commenter calls it keyword-driven content that pollutes the internet.
101HN people shared it
13linked it in comments
857stories submitted
4.9Kstory points
5 years agolast shared
2017people per year2026
Most-shared posts
- 1Deep learning has a size problem5 people · 144 points · first shared 2019 · 45 comments on HN
- 2The New iPad Pro’s Lidar Sensor Changes the Augmented Reality Game4 people · 55 points · first shared 2020 · 9 comments on HN
- 3ARM-Based Apple Silicon Changes Mac Forever4 people · 16 points · first shared 2020 · 4 comments on HN
- 4A 2019 Guide to Speech Synthesis with Deep Learning3 people · 140 points · first shared 2019 · 20 comments on HN
- 5Capsule Networks: A new and attractive AI architecture3 people · 19 points · first shared 2018 · discussion on HN
- 6Building Not Hotdog with Turi Create and Core ML – in an Afternoon3 people · 11 points · first shared 2017 · discussion on HN
- 7Artificial art: How GANs are making machines more creative3 people · 5 points · first shared 2019 · discussion on HN
- 8Can artificial intelligence be taught how to joke?3 people · 4 points · first shared 2019 · discussion on HN
- 9On-device training in Core ML 3 and why it matters for developers3 people · 4 points · first shared 2019 · discussion on HN
- 10OpenCV-Python Cheat Sheet: From Importing Images to Face Detection2 people · 179 points · first shared 2019 · 14 comments on HN
- 11Open-Source Machine Learning Repos to Inspire Your Next Project2 people · 137 points · first shared 2018 · 7 comments on HN
- 12The Ancient Secrets of Computer Vision: The Evolution of Human Vision2 people · 83 points · first shared 2019 · 9 comments on HN
- 13Why data scientists should start learning Swift2 people · 62 points · first shared 2018 · 66 comments on HN
- 14Build your own 'Portrait Mode' using machine learning on iOS in ~30 minutes2 people · 40 points · first shared 2018 · discussion on HN
- 15Top machine learning projects in 2018: Reddit edition2 people · 17 points · first shared 2018 · discussion on HN
What HN people wrote when they linked it
jamesonthecrow · 2018-10-10
Core ML is going to be your best bet. Most training is still done server side using frameworks like TensorFlow, Keras, and PyTorch. Once you've trained your model, you can convert it to Core ML with coremltools or export it to Core ML directly if the platform supports it.
Apple has a couple tools, Turi Create and Create ML, to train ML models specifically for mobile use, but their not nearly as fully featured or widely used.
If you're interested specifically in mobile ML, check out https://heartbeat.fritz.ai. We've got a bunch of resources for mobile machine learning. If you're looking for ready-to-use models or tools to manage them in your app, check out Fritz (https://fritz.ai). Disclaimer, I'm a founder at Fritz which sponsors Heartbeat. Happy to answer any questions!
aw3c2 · 2019-04-20
You are right, I would not say that to a person in meatspace and I should not say things like that to people on the internet either.
But:
Looking at their other articles gives me the impression of a content creator that just pollutes the internet for keywords, potentially paid-for.
Just look at https://heartbeat.fritz.ai/ , which this random assortment of code snippets was compiled for, it's all just keyword stuffing as promotion for fritz.ai, plain and simple.
And oh look, nicely delayed submissions of article after article by OP: https://news.ycombinator.com/submitted?id=salma-ghoneim
sandreas · 2021-04-14
kordlessagain · 2021-01-18
From 2019: https://heartbeat.fritz.ai/deep-learning-has-a-size-problem-ea601304cd…
> Earlier this year, researchers at NVIDIA announced MegatronLM, a massive transformer model with 8.3 billion parameters (24 times larger than BERT)
> The parameters alone weigh in at just over 33 GB on disk. Training the final model took 512 V100 GPUs running continuously for 9.2 days.
Running this model on a "regular" machine at some useful rate is probably not possible at this time.
11thEarlOfMar · 2021-01-08
I was referring to this supercomputer:
https://heartbeat.fritz.ai/computer-vision-at-tesla-cd5e88074376
Someone · 2020-11-24
As a developer, you can. The APIs exist (https://developer.apple.com/documentation/vision/vnrecognizetextreques… seem simple to use (https://www.hackingwithswift.com/example-code/vision/how-to-use-vnreco… and appear to perform decently (https://heartbeat.fritz.ai/comparing-apples-and-google-s-on-device-ocr… compares with Google’s on-device OCR)
So, finally, they don’t step on the toes of third-party developers, and you still complain? ;-)
andrewrothman · 2020-02-13
Both OpenGL shader code and on-device ML can be done in React Native. However, you likely won't reach full native performance today.
https://www.youtube.com/watch?v=1HYnrdKEVMU
https://www.youtube.com/watch?v=LbcuLfrt8XA
https://heartbeat.fritz.ai/image-classification-on-react-native-with-t…
You're right there will always be a very high demand for native engineers to build the underlying bridge code to back these capabilities. As cross-platform tools gain new bridged capabilities and become faster (https://engineering.fb.com/android/hermes/), as they have been slowly, I think the use-cases where using something like React Native or Flutter is possible and makes sense will grow.
ghop02 · 2019-09-30
Core ML 3 adds a ton of new operations, including control flow support.
https://heartbeat.fritz.ai/whats-new-in-core-ml-3-d108d352e50a
cmenge · 2019-07-13
Not an expert, but this is also my understanding: The nerves are more like a neural network, not just wiring to the brain: https://heartbeat.fritz.ai/the-ancient-secrets-of-computer-vision-2-by…
MegaBinaryTable · 2019-05-14
See https://news.ycombinator.com/item?id=17050436 for past discussion and link to the original paper mentioned in the article.
There's also a similar article which discusses the same paper:
https://heartbeat.fritz.ai/why-do-neural-networks-work-5545cfce1225
hindsightbias · 2019-04-22
mtmail · 2018-06-01
From my bookmarks, I haven't taken any of those courses (yet) https://heartbeat.fritz.ai/new-to-data-science-here-are-a-few-places-t…
hsiaoer · 2018-02-19
Awesome. Been meaning to play around with Turi Create. Seems pretty easy to get started with. One of my colleagues wrote a post on building the hotdog/not hotdog app with it in an afternoon. https://heartbeat.fritz.ai/building-not-hotdog-with-turi-create-and-co…