Working with neural networks can be quite tricky, especially if you're trying to build them from scratch. Instead of manually coding algorithms and formulas, you can use ready-made tools such as the sklearn library.

To create the same model as in this section, we can use the MLPClassifier class from the sklearn library:

Note

Neural networks in sklearn determine the number of inputs and outputs based on the data they are trained on. Therefore, there is no need to set them manually.

Benefits of using libraries to build neural networks:

1. Ease of use: You don't have to dive deep into the details of each algorithm. You can simply use ready-made methods and classes;
2. Optimization: The sklearn library is optimized for performance, which can reduce the training time of your model;
3. Extensive Documentation: sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;
4. Compatibility: sklearn integrates well with other popular Python libraries such as numpy, pandas and matplotlib.

Here you can see basic commands of MLPClassifier usage:

Changing model parameters:

Training the model:

Predict output values: