# GAMSPy and Machine Learning#

GAMSPy heralds a new era of possibilities, bridging the gap between machine learning and optimization that was previously difficult to cross using GAMS alone. Here’s why GAMSPy is the ultimate choice:

Easy to understand, easy to write

`eq1[...] = y == tanh(a @ W + b)`

Versatility in Solver Selection

`regression.solve(solver="your_favourite_solver")`

It provides a robust algebraic language that allows you to experiment with how a neural network is implemented.

Built-in flexibility:

You are not limited to inference; you can also train your neural network.

You can build the architecture from scratch using GAMSPy

Development speed of Python combined with model generation speed of the GAMS execution engine

Equations and variables are generated in GAMS, not in Python, giving GAMSPy a speed advantage.

We are continually developing our ML-related features. If you have specific needs or require additional information, please use our Discourse platform.