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arathore

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If by running models you mean just the inference phase, then even today you can run large family of ML models on commodity hardware (with some elbow grease, of course). The training phase is generally the one not easily replicated by non-corporations.

Python 3.11 5 years ago

When printing tracebacks, the interpreter will now point to the exact expression that caused the error instead of just the line. For example:

  Traceback (most recent call last):
    File "distance.py", line 11, in <module>
      print(manhattan_distance(p1, p2))
            ^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "distance.py", line 6, in manhattan_distance
      return abs(point_1.x - point_2.x) + abs(point_1.y - point_2.y)
                             ^^^^^^^^^
  AttributeError: 'NoneType' object has no attribute 'x'
This is such a great quality of life improvement.

I am looking forward to performance improvements. It would be great if the core window services were optimized to use less resources to improve the windows experience on less impressive hardware.

For those who might not be familiar, graph subsampling is a method of extracting smaller graphs from a large graph while preserving some notion of overall structure. The subsampling can be focused on finding representative nodes, edges or combinations of those (as well as optimizing for some other local or global properties).

It has applications wherever large graphs are encountered - social networks, biological processes etc. An example application of the library could be as follows - you have a very large graph and you want to do spectral clustering. Throwing spectral clustering directly at such large graphs might not be feasible, so one can first perform a node based subsampling and then apply spectral clustering on the smaller graph on the representative nodes found by the subsampling.

The jitter artifacts could be removed by applying some kind of temporal consistency constraints on the network predictions. But such an approach will also definitely introduce a lot of edge cases where a sudden change is actually ignored due to enforcement of the constraint. An adaptive consistency parameter could probably work better, but it's non-trivial to have a meta-algorithm to figure out the paramter.

Such competitions do not usually result in a comprehensive "solution" by themselves - pushing the state-of-the-art is more common. Also the value is not going to be derived solely from the algorithm but more from its deployment to real world applications and the surrounding infrastructure to make it possible.

Looks cool. Iirc the transformer architecture doesn't allow any constraints on the learned language model. For code completion settings, a model aware of the (programming) language constructs explicitly and then augmented with code samples would be much more efficient (you could greatly reduce the search space for next token etc).

The article talks specifically about usability testing - though it isn't in the title but it is in the first line of the article. I don't think pricing strategies or matching algorithms and such would fall under this domain.

Wow! I am sure that I would want to integrate some of the ideas from your project especially the servo motors and IR emitters since I don't want to replace the existing hardware in my house.

Thanks, I have been looking at subreddits and youtube videos like you have mentioned. I posted this here because I feel responses here would help me have a better filter for stuff I can find online since there can be a lot of things that can be done horribly wrong :)

"First-time owners in their 20s or early 30s, accustomed to instant gratification and one-day Prime delivery, often expect immediate cures for animal ailments, she said." Unfair to call the concern about your pet's health and wishing for their faster recovery akin to instant gratification from online shopping IMO.