hyperopt

5 ms·

1.
by hyperopt 2y ago
Charles Petzold wrote one of my favorite books - "Code: The Hidden Language of Computer Hardware and Software". Very excited to see how this turns out and thanks for giving some of this knowledge away for free!
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Changing the language of an LLM response with a vector addition(corywalker.me)
1 points by hyperopt 2y ago|0 comments
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by hyperopt 3y ago
The demo for this is great. It's the best non-corporation assistant I've used so far. I suspect most of the gains here relative to the Alpaca model might have to do with the fact that the ShareGPT data are full conversations. They
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by hyperopt 3y ago
Not even close.
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by hyperopt 3y ago
Does anyone know of any good test suites we can use to benchmark these local models? It would be really interesting to compare all the ones capable of running on consumer hardware so that users can easily choose the best ones to use. Curren
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by hyperopt 4y ago
Fantastic! I was involved in the freemyipod project back when the 4G was still a current product. In fact, the freemyipod.org wiki ran from a server I kept downstairs in my parent's home. My favorite memory was when we all built robots
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by hyperopt 8y ago
Discussion on this is already at https://news.ycombinator.com/item?id=18858724
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by hyperopt 8y ago
Nice! Extending this further to support associative and commutative operations yields a functionality similar to that provided in Mathematica. This approach was taken by the MatchPy project to hopefully integrate into SymPy so that they can
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Show HN: Expreduce 0.4, a free Mathematica-compatible algebra system in Go(corywalker.github.io)
4 points by hyperopt 8y ago|0 comments
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by hyperopt 9y ago
In CA, Quest Diagnostics requires a doctors note for the tests I've taken. Other providers or other tests may be different.
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Show HN: Expreduce 0.2, a free Mathematica-compatible CAS written in Go(github.com)
14 points by hyperopt 9y ago|1 comments
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Show HN: Expreduce, a language for math written in Go(github.com)
3 points by hyperopt 10y ago|0 comments
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by hyperopt 10y ago
That's what I'm thinking. I was anticipating the release of GPU instances, but now I'm thinking that they will simply leapfrog over GPU instances straight to this.
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by hyperopt 10y ago
In March at the GCP NEXT keynote [1], Jeff Dean demos Cloud ML on the GCP. He casually mentions passing in the argument "replicas=20" to get "20 way parallelism in optimizing this particular model". GCE does not currentl
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by hyperopt 10y ago
It doesn't just need to be a place to experiment cheaply. Many companies building software around ML techniques still rent time on EC2. Unless you are training models 24/7 and have your machines located in a very cost efficient lo
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by hyperopt 10y ago
I listen to the Talking Machines as well. Great podcast. Another question would be are the gains worth the cost of an ML-specific ASIC. GPUs have the entire, massive gaming industry driving the cost down. I suppose that as adoption of gradi
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by hyperopt 10y ago
The Cloud Machine Learning service is one that I'm highly anticipating. Setting up arbitrary cloud machines for training models is a mess right now. I think if Google sets it up correctly, it could be a game changer for ML research for