Tutorials ========= To get started with Aidge, please follow the `Aidge 101 tutorial `_. This tutorial demonstrates the basic features of the Aidge Framework, importing an ONNX, transforming a neural network graph, performing inference and a cpp export. For an advanced usage of the Aidge Framework, please refer to the following tutorials. Aidge DNN fonctionnalities -------------------------- - `Manipulating databases and creating batches of data `_ - 🚧 Train a Deep Neural Network - `Provide an operator implementation using Python or atomic operators `_ - `Perform advanced graph matching with the Graph Regular Expression tool `_ Optimize your DNN with Aidge ---------------------------- - 🚧 Optimize your neural network with Post Training Quantization - `Optimize the inference of your neural network with Tiling `_ Export your DNN with Aidge -------------------------- - `Add a custom implementation for a cpp export `_ - `Export your DNN with TensorRT `_ - 🚧 Export your DNN for an STM32 Contributing to Aidge --------------------- You will find all information for contributing to Aidge in the `wiki `_. For example, you can help us extend our operator coverage by adding an operator and its implementation in the Aidge library. The `Add an operator and its implementation Tutorial `_ details the steps to follow. If you encounter any difficulty with the Tutorials, please create an issue `here `_.