TsetlinMachine
deep-learning-drizzle
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| TsetlinMachine | deep-learning-drizzle | |
|---|---|---|
| 3 | 1 | |
| 449 | 11,764 | |
| 2.0% | - | |
| 3.4 | 0.0 | |
| about 1 month ago | 3 months ago | |
| Cython | HTML | |
| MIT License | - |
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TsetlinMachine
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[D] Are there unconventional cognition architectures that learn without SGD, weights between neurons, or can only be done on the CPU?
But for unconventional / back to the roots Tsetlin machines is a candidate, https://github.com/cair/TsetlinMachine
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Study reveals that animals cope with environmental complexity by reducing the world into a series of sequential two-choice decisions and use an algorithm to make a decision, a strategy that results in highly effective decision-making no matter how many options there are
Reminds me of Tsetlin machines
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[R] Drop Clause boosts Tsetlin Machine accuracy up to +4% and training speed up to 4x
Hi u/pddpro, there are unfortunately few tutorials available. There are some video resources here: https://github.com/cair/TsetlinMachine#videos and some demos here: https://github.com/cair/pyTsetlinMachine. Currently writing a book: Introduction to Tsetlin Machines. Plan to share drafts of the chapters here as I proceed, including tutorials.
deep-learning-drizzle
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Consolidated Video lectures for Machine Learning(including DL, CV, NLP, etc)
Also this as well for whoever needs it
What are some alternatives?
fim - FIM is an Open Source Host-based file integrity monitoring tool that performs file system analysis, file integrity checking, real time alerting and provides Audit daemon data.
cs229-solution - CS229 Solution (summer 2019, 2020).
pyTsetlinMachine - Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and literal budget
OPUS-MT-train - Training open neural machine translation models
GODM
ultimate-volleyball-starter - Tutorial kit for building a 3D deep reinforcement learning environment with Unity ML-Agents.
tmu - Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
awesome-full-stack-machine-learning-courses - Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
ta-lib-python - Python wrapper for TA-Lib (http://ta-lib.org/).
bidd-molmap - MolMapNet: An Efficient ConvNet with Knowledge-based Molecular Represenations for Molecular Deep Learning
deep-rl-class - This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.
Contour-Based-Writing - This is a simple concept to do writing like operation using the contours. Please follow the article https://q-viper.github.io/2020/08/28/gesture-based-visually-writing-system-web-app/ for further details.