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.

Benefits of Using sklearn

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.

Perceptron in sklearn

To create the same model as in this section, you can use the MLPClassifier class from the sklearn library. Its key parameters are as follows:

• max_iter: defines the maximum number of epochs for training;
• hidden_layer_sizes: specifies the number of neurons in each hidden layer as a tuple;
• learning_rate_init: sets the learning rate for weight updates.

For example, with a single line of code, you can create a perceptron with two hidden layers of 10 neurons each, using at most 100 epochs for training and a learning rate of 0.5:

As with our implementation, training the model simply involves calling the fit() method:

To get the predicted labels (e.g., on the test set), all you have to do is call the predict() method: