TensorFlow ML is a project that aims to provide an abstract implementation of commonly used machine learning algorithms using TensorFlow, without relying on external libraries like scikit-learn. This project is designed to offer a comprehensive and flexible set of machine learning models that can be used for various tasks, such as classification, regression, clustering, and more.
Getting started
You can install TensorFlow ML from PyPI using pip. Follow these steps:
pip install tensorflow-ml
Alternatively, to use TensorFlow ML from GitHub directly, follow these steps:
Clone the GitHub repository: git clone https://github.com/siddhantpathakk/tensorflow-ml.git
Install the required dependencies by running: pip install -r requirements.txt
After the installation is complete, you can import the implemented machine learning models in your Python scripts and start using TensorFlow ML for your machine learning tasks.
As of the latest release, support for Tensorflow ML on Google Colab works with some minor issues. It shall show some error regarding existing installations and version requirements to match google-colab library. The library gets installed successfully and is usable nonetheless. We are working on resolving these issues, and we hope to release a stable version soon.
