perturbation

6 ms·

1.
by perturbation 3mo ago
Location: Dallas / Fort Worth, TX, USA Remote: Yes Willing to relocate: No Technologies: PyTorch, Tensorflow 2 / Keras, Rust, Go, Python. Model training, deployment, LLMs/SLMs. Docker/OCI, Helm and K8s (some). PostgreS
2.
by perturbation 2y ago
I think a lot of these may have improved since your last experience with Keras. It's pretty easy to override the training loop and/or make custom loss. The below is for overriding training / test step altogether, custom loss
3.
by perturbation 4y ago
The big thing that PyTorch Mobile is lacking compared to TF Lite is on-device accelerator support (GPU/DSP/etc.) (there's experimental support for NNAPI https://pytorch.org/tutorials/prototype/nnapi_
4.
by perturbation 6y ago
Let's hope not, I like autodiff (and this project :( ).
5.
by perturbation 6y ago
> To return to the point about image augmentations being hard to add: It's so easy to explain what your training code should do "Just distort the hue a bit" and there seem to be operations explicitly for that: https:&#x2F
6.
by perturbation 7y ago
If you don't mind getting your hands dirty a bit, I think Nvidia's model [Jasper]( https://arxiv.org/pdf/1904.03288.pdf ) is near SOTA, and they have [pretrained models]( https://ngc.nvidia.com/c
7.
by perturbation 7y ago
This is cool - might be worth training a simple discriminator model to identify your utterances, and then you can use the plug-and-play language model (PPLM - https://github.com/huggingface/transformers/blob&#x2F
8.
by perturbation 7y ago
Ah, thank you for explaining! That makes sense.
9.
by perturbation 7y ago
Additionally, the Nim code was not compiled with many optimizations turned on! (I.e., without -d:release). $ nim c -o:base64_test_nim -d:danger --cc:gcc --verbosity:0 base64_test.nim $ nim c -o:json_test_nim -d:danger --cc:gcc -
10.
by perturbation 7y ago
Another thing I noticed: the Nim code was compiled without the -d:release flag. For example, the JSON test was compiled with: $ nim c -o:json_test_nim -d:danger --cc:gcc --verbosity:0 json_test.nim I don't think that the -d:da
11.
by perturbation 7y ago
I'd recommend Elements of Statistical Learning or ISLR instead, if you want to start with a theory-heavy introduction. Most of what you need for DS you'd I think better learn through projects or on-the-job. Also, as others have m
12.
by perturbation 7y ago
Congrats guys!!! This has been much-anticipated and I'm very excited. I personally wish that the owned reference stuff ( https://nim-lang.org/araq/ownedrefs.html ) had been part of 1.0, but I think that at some p
13.
by perturbation 7y ago
> * They measure the wrong things that reward the network. Because the dataset is imbalanced you can't use an ROC curve, sensitivity, or specificity. You need to use precision and recall and make a PR curve. This is machine learning
14.
by perturbation 7y ago
https://github.com/yuki24/did_you_mean#installation : Ruby 2.3 and later ships with this gem and it will automatically be required when a Ruby process starts up. No special setup is required. It doesn't c
15.
by perturbation 7y ago
AutoML is essentially training a ML model using some heuristics or optimization algorithm to select model architecture and train a model. Feature engineering / feature synthesis as well as interpretability remain open challenges. If I
16.
by perturbation 7y ago
Disclaimer: I really love Nim and have written a fair amount in it, but I'm not a language designer guy. I like that this will make multithreading easier with shared memory, but I worry this will make the language more complex and dela
17.
by perturbation 8y ago
Are you using reticulate ( https://github.com/rstudio/reticulate ), or having Python spawn a new worker process for R?
18.
by perturbation 8y ago
Location: Dallas, TX Remote: Yes Technologies: Keras, R, Python, and Spark are what I use ever day. Jupyter, scikit-learn, spaCy, NLTK, H2O, mlr, caret, prophet, tsfresh, ggplot, and tidyverse packages are libraries that I use commonly. I
19.
by perturbation 8y ago
Dplyr works great with SQL (both SQLite and others).
20.
by perturbation 8y ago
Their example with LightGBM ( https://nni.readthedocs.io/en/latest/gbdt_example.html ) is very cool - I wanted to put together a custom script with mlflow + catboost + mlrMBO to do something similar, but this puts e
21.
by perturbation 8y ago
Haven't looked at MyCroft before. It looks like MyCroft exposes less of the nuts-and-bolts of modeling? I'm not sure where I would plug in a custom entity extraction or intent detection model, but I do see that it lets you add c
22.
by perturbation 8y ago
I'll +1 SNIPs or Rasa, they're both really nice. It looks like the NLU part of Leon is a logistic regression classifier ( https://github.com/leon-ai/leon/blob/360d1020c4bd8bf1df37646... ) so it'
23.
by perturbation 8y ago
I think they want data scientist to do plain-old-data-engineer work, but not just plain-old-data-engineer work. Getting / cleaning data is part of the job description, IMHO. You can't be a data scientist and entirely reliant o
24.
by perturbation 8y ago
I have been a data scientist for the last 4 years. I think (one of) the problems with the data science career field is that there are a lot of juniors who want to run sklearn and call it a day, following the tutorials that seem to 'jus
25.
by perturbation 8y ago
To some extent, R works like this... packages are only on CRAN if they pass automatic checks and there's a pretty strong culture of testing with testthat. You can have your own package on Github or external repo, but then that's
26.
by perturbation 8y ago
I'm hoping that this results in a nice, high-level API for https://github.com/fragcolor-xyz/nimtorch as well, which AFAIK has been wrapping the low-level Aten API. I've been keeping my eye on that project, a
27.
by perturbation 8y ago
I've used plumber, and it's pretty easy to get started, though doesn't feel very polished. Handling multipart form data took some hackarounds with the underlying "Rook" package. I'm curious to see how https:&
28.
by perturbation 8y ago
It would be nice to benchmark the text extraction to a baseline method, say with Apache Tika ( https://tika.apache.org/ ). I would expect the deep learning approach to outperform traditional approaches in terms of accuracy, b
29.
by perturbation 8y ago
Kind of reminds me of https://github.com/timothycrosley/hug , especially using type hints for validation of request parameters.
30.
by perturbation 8y ago
See also https://github.com/tdda/tdda , which has some of the same features and can run tests against new data. It runs on Pandas dataframes, CSVs, and can generate hand-editable JSON constraints for a dataset.

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