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Tmu Alternatives
Similar projects and alternatives to tmu
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Scout Monitoring
Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
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nvitop
An interactive NVIDIA-GPU process viewer and beyond, the one-stop solution for GPU process management.
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InfluxDB
Purpose built for real-time analytics at any scale. InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards.
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catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better tmu alternative or higher similarity.
tmu discussion
tmu reviews and mentions
Posts with mentions or reviews of tmu.
We have used some of these posts to build our list of alternatives
and similar projects.
- Tsetlin machine – the other AI toolbooks
- Tsetlin Machine Unified (TMU) - One Codebase to Rule Them All
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[R] New Tsetlin machine learning scheme creates up to 80x smaller logical rules, benefitting hardware efficiency and interpretability.
Code: https://github.com/cair/tmu
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This Artificial Intelligence (AI) Research From Norway Introduces Tsetlin Machine-Based Autoencoder For Representing Words Using Logical Expressions
Quick Read: https://www.marktechpost.com/2023/01/10/this-artificial-intelligence-ai-research-from-norway-introduces-tsetlin-machine-based-autoencoder-for-representing-words-using-logical-expressions/ Paper: https://arxiv.org/pdf/2301.00709.pdf Github: https://github.com/cair/tmu
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Do we really need 300 floats to represent the meaning of a word? Representing words with words - a logical approach to word embedding using a self-supervised Tsetlin Machine Autoencoder.
Here is a new self-supervised machine learning approach that captures word meaning with concise logical expressions. The logical expressions consist of contextual words like “black,” “cup,” and “hot” to define other words like “coffee,” thus being human-understandable. I raise the question in the heading because our logical embedding performs competitively on several intrinsic and extrinsic benchmarks, matching pre-trained GLoVe embeddings on six downstream classification tasks. Thanks to my clever PhD student Bimal, we now have even more fun and exciting research ahead of us. Our long term research goal is, of course, to provide an energy efficient and transparent alternative to deep learning. You find the paper here: https://arxiv.org/abs/2301.00709 , an implementation of the Tsetlin Machine Autoencoder here: https://github.com/cair/tmu, and a simple word embedding demo here: https://github.com/cair/tmu/blob/main/examples/IMDbAutoEncoderDemo.py.
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A note from our sponsor - Scout Monitoring
www.scoutapm.com | 14 Sep 2024
Stats
Basic tmu repo stats
5
119
8.3
20 days ago
cair/tmu is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of tmu is Python.
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