pycm
ANN-decompiler
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| pycm | ANN-decompiler | |
|---|---|---|
| 18 | 6 | |
| 1,429 | 20 | |
| - | - | |
| 5.0 | 0.0 | |
| 2 months ago | over 2 years ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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pycm
- PyCM 4.0 Released: Multilabel Confusion Matrix Support
- PyCM 3.9 Released: Log-loss Support
- PyCM 3.8 Released: Distance/Similarity Support
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[P] PyCM 3.7 released: ROC curve and Precision-Recall curve are added
The complete change log of this version is available here.
- PyCM 3.7 Released: ROC/PR Curve Support
- PyCM 3.6 released: Multi-class confusion matrix library in Python
- PyCM 3.5 released: Multi-class confusion matrix library in Python
- PyCM 3.4 released: Multi-class confusion matrix library in Python
- [P] PyCM 3.3 released: Comparison of Classifiers Based on Confusion Matrix
ANN-decompiler
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ChatGPT refuses to create a poem admiring Donald Trump but creates a poem and admires Joe Biden. ChatGPT is built in with political biases.
I can understand to why they would say that, as to what they refer as "woke" has massively invaded everything, but you are probably right that it was not done on purpose. Its not that easy to filter things like that out, especially not if humans have to do that manually. Also a friendly reminder that this is not real artificial intelligence, there is a good resource here https://github.com/Shamar/ANN-decompiler that explains this a bit better to why its mostly a magic trick, we have done models like that on paper in the 70's, not as large as those but its not exactly a new trick.
- “AI” Demystified: A Decompiler
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“AI” demystified: a decompiler for “artificial neural networks”
> Does this somehow smuggle the training dataset back into the VM?
Turns out you were right about this: http://www.tesio.it/2021/09/01/a_decompiler_for_artificial_n...
Obviously I was not aware of this, so the whole decompilation process was a waste of computation time, but it doesn't prove nor disprove anything about the "model"'s relation with the source dataset.
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[R] "AI" demystified: a decompiler
But I would be very happy to learn from you how to compute the whole source dataset from the output produced by compile.py without considering the "model".
What are some alternatives?
seq2seq - A general-purpose encoder-decoder framework for Tensorflow
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
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AI-Expert-Roadmap - Roadmap to becoming an Artificial Intelligence Expert in 2022
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InvoiceNet - Deep neural network to extract intelligent information from invoice documents.
intelligent-trading-bot - Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
0xDeCA10B - Sharing Updatable Models (SUM) on Blockchain