| ML/DL Technologies | Forward Neural Networks; Deployment in C |
|---|
| Science Fields | Generic |
|---|
| Difficulty | Medium |
|---|
| Language | English |
|---|
| Type | fully annotated |
|---|
Software and Tools
| Programming Language | Python, C |
|---|
| ML Toolset | Keras + Tensorflow 1.x |
|---|
| Additional libraries | scikit-learn |
|---|
| Suggested Environments | INFN-Cloud VM, bare Linux Node, Google CoLab |
|---|
| Data Creator | scikit-learn community |
|---|
| Data Type | image |
|---|
| Data Size | < 1 GB |
|---|
| Data Source | scikit-learn package |
|---|
In the life of a physicist, the time arrives when you get disgusted by the software dependencies that evaluating a simple neural network requires and you want the plain, pure and clean function to be evaluated in a whatever context.
...
The other reason is that the original data that we used to train this are not public.
Download and run the jupyter notebook: https://github.com/landerlini/MLINFN-TutorialNotebooks/blob/master/LHCbMasterclassExplained.ipynb
...