TrainDeep is a web based tool designed to generate library code for deploying neural networks on microcontroller platforms. Its primary function is to translate a configured neural network into target specific code that can run on resource constrained embedded devices. The tool supports multiple output formats, including Python and MicroPython as well as C and C++ for Arduino sketches (Ino) and hardware abstraction layer (HAL) or standard C. This allows developers to integrate machine learning models into a variety of microcontroller based systems. The main capabilities of TrainDeep focus on configuring a neural network architecture suitable for low memory environments, with a maximum model size of approximately 1 MB. Users define the network structure and parameters, and the tool then generates the corresponding library files that can be compiled and flashed onto the target hardware. The generated code is intended to handle inference tasks without requiring a full machine learning framework on the device. Typical use cases include embedded machine learning applications where sensor data is processed locally on a microcontroller, such as in IoT devices, wearable electronics, or smart home controls. The workflow involves selecting the target platform and language, specifying the neural network configuration, and downloading the generated library for integration into a larger firmware project. Technical requirements depend on the chosen output format, but all target devices must have sufficient memory and computational capacity to accommodate the generated code. By abstracting the low level implementation details, TrainDeep enables engineers and researchers to prototype and deploy neural network models on microcontrollers without manual coding of inference routines. The tool is accessible through its website at traindeep.ai.