pyod
deep_learning_and_the_game_of_go
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pyod | deep_learning_and_the_game_of_go | |
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7 | 3 | |
7,941 | 929 | |
- | - | |
7.7 | 0.0 | |
4 days ago | over 1 year ago | |
Python | Python | |
BSD 2-clause "Simplified" License | - |
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pyod
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A Comprehensive Guide for Building Rag-Based LLM Applications
This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod
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Analyze defects and errors in the created images
PyOD
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Multivariate Outlier Detection in Python
Check out the algorithms and documentation in this toolkit. It’ll give you a list of methods to read up on to understand their mechanisms. https://github.com/yzhao062/pyod
- Pyod – A Comprehensive and Scalable Python Library for Outlier Detection
- Predictive Maintenance and Anomaly Detection Resources
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[D] Unsupervised Outlier Detection - Advise Requested
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
- PyOD: ~50 anomaly detection algorithms in one framework.
deep_learning_and_the_game_of_go
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Training an AI for Tigris and Euphrates
A good book I found is https://www.manning.com/books/deep-learning-and-the-game-of-go
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Why do engines often evaluate completely winning endgame positions between +60 and +63? What's significant about the low 60's as an evaluation? Or is it just a placeholder when the computer can't quite find a forced mate?
If you want to understand how the new approach used by Leela Zero and Alpha Zero works, the book Deep Learning and the Game of Go is fun and easy to read. Although it's about Go rather than chess, most of the contents are equally relevant to chess.
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[Q] Deep Learning and the Game of Go - anyone got the code to work?
One of the first hits pointed me to this github repo: https://github.com/maxpumperla/deep_learning_and_the_game_of_go
What are some alternatives?
tods - TODS: An Automated Time-series Outlier Detection System
deepxde - A library for scientific machine learning and physics-informed learning
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
GeneticAlgorithmPython - Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
alibi-detect - Algorithms for outlier, adversarial and drift detection
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
pycaret - An open-source, low-code machine learning library in Python
pycox - Survival analysis with PyTorch
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Keras - Deep Learning for humans
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python