# [Github Codebase for Tsetlin Machine Implementations](https://www.literal-labs.ai/research/tsetlin-machine-github.html)

## Dive into TMU for versatile Tsetlin Machine models, from basic setups to advanced AI applications, all in one unified repository.

**Authors**: include [Ole-Christoffer Granmo](https://www.literal-labs.ai/about/ole-christoffer-granmo.html)  
**Published**: 2024, _Github_

TMU serves as a unified codebase, a one-stop repository offering diverse Tsetlin Machine implementations suitable for a variety of AI tasks, making it a treasure trove for researchers and AI enthusiasts.

The Tsetlin Machine Unified (TMU) repository stands out as a comprehensive platform hosting multiple implementations of Tsetlin Machines, each tailored for different aspects of machine learning. From basic to advanced structures like the Convolutional and Regression Tsetlin Machines, the repository is well-equipped to support both standard and cutting-edge AI research. Upcoming features include a Multi-task Classifier and One-vs-one Multi-class Classifier, alongside innovative methods like Focused Negative Sampling and Type III Feedback.

TMU supports continuous feature handling and offers specialized constructs like TMComposites for collaboration between different Tsetlin Machine types. For those looking to dive deep into machine learning at a hardware-accelerated pace, TMU provides wrappers for both C and CUDA, ensuring high-performance computations across platforms.

With its broad spectrum of features and extensions, alongside robust development support and detailed guides for setup and usage, TMU is positioned as an indispensable resource for advancing research and development in the field of machine learning with Tsetlin Machines.

### Read more

View the _Tsetlin Machine Unified - Github Code Base_ repository at [Github](https://github.com/cair/tmu/).

### Other research by our team

**[An Optimised Toolbox for Advanced Image Processing with Tsetlin Machine Composites](https://www.literal-labs.ai/tsetlin-machines/cifar-10-benchmarks.html)**

[](https://www.literal-labs.ai/tsetlin-machines/cifar-10-benchmarks.html)

**[MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications](https://www.literal-labs.ai/research/tsetlin-machine-system-on-chip.html)**

[](https://www.literal-labs.ai/research/tsetlin-machine-system-on-chip.html)

**[REDRESS: Generating Compressed Models for Edge Inference Using Tsetlin Machines](https://www.literal-labs.ai/research/compress-tsetlin-machine-automata.html)**

[](https://www.literal-labs.ai/research/compress-tsetlin-machine-automata.html)

**[Systematic Search for Optimal Hyper parameters of the Tsetlin Machine on MNIST Dataset](https://www.literal-labs.ai/research/optimise-hyperparameters-tsetlin-machine-mnist.html)**

[](https://www.literal-labs.ai/research/optimise-hyperparameters-tsetlin-machine-mnist.html)

**[Visualization of Machine Learning Dynamics in Tsetlin Machines](https://www.literal-labs.ai/research/tsetlin-machine-visualisation.html)**

[](https://www.literal-labs.ai/research/tsetlin-machine-visualisation.html)

**[Self-timed reinforcement learning using Tsetlin Machine](https://www.literal-labs.ai/research/tsetlin-machine-reinforcement-learning.html)**

[](https://www.literal-labs.ai/research/tsetlin-machine-reinforcement-learning.html)

**[Learning automata based energy-efficient AI hardware design for IoT applications](https://www.literal-labs.ai/research/royal-society-ai-automata.html)**

[](https://www.literal-labs.ai/research/royal-society-ai-automata.html)