This Jupyter notebook contains instructions and code for training an artificial neural network. The notebook covers various aspects of the training process, such as:
- Loading and preprocessing data
- Defining the neural network architecture
- Setting hyperparameters
- Implementing training algorithms
- Evaluating the performance of the trained model
Additionally, the notebook includes visualizations of the training process and the results obtained.
To run this notebook, you will need to have the following software and libraries installed:
- Jupyter Notebook
- Python
- Required Libraries (e.g. Tensorflow, Keras, Numpy, Pandas, Matplotlib)
This notebook was created as a task in a Master's degree in Complexity Sciences, where the study of artificial neural networks is used as a tool to analyze complex systems and understand their behavior.