Summary

Concept of a Neural Network

A neuron is the fundamental unit of information processing in a neural network. It takes inputs, processes them, and produces an output.

Each input to a neuron is assigned a weight, which determines its importance in the computation. A bias is an additional parameter that helps shift the neuron's output, providing flexibility in learning patterns.

Training a neural network involves adjusting these weights and biases to minimize errors and improve accuracy.

The activation function transforms the sum of weighted inputs into the neuron's output. Common activation functions include:

1. Sigmoid Function: outputs values between 0 and 1, useful for binary classification;
2. ReLU (Rectified Linear Unit): helps deep networks train efficiently;
3. Hyperbolic Tangent (tanh): outputs values between -1 and 1, making it useful for zero-centered data.

During forward propagation, information flows from the input layer through the hidden layers to the output layer, where a prediction or inference is made.

To improve predictions, backpropagation is used. This process propagates error information backward through the network, adjusting the weights to reduce errors.

Building a Neural Network from Scratch

A multilayer perceptron (MLP) consists of multiple layers:

1. Input Layer: receives the input data;
2. Hidden Layers: process the data and extract patterns;
3. Output Layer: produces the final prediction or classification.

Each layer contains multiple neurons, and the output from one layer serves as the input for the next.

Backpropagation consists of forward propagation, error computation, gradient calculation, and weight and bias adjustment.

The learning rate is a key parameter in gradient descent, controlling how much the weights update during training. A higher learning rate speeds up training but can cause the model to miss important patterns, while a lower learning rate ensures more precise learning but may slow down convergence.

There are several ways to evaluate a model's performance, including:

• Accuracy: measures the percentage of correct predictions;
• Mean Squared Error (MSE): evaluates error for regression tasks;
• Cross-Entropy: commonly used for classification problems.

Implementing a Neural Network Using Scikit-Learn

The first step is to create a model:

Once the model is created, it should be trained on the training set:

Finally, predictions can be made, for example, on a test set:

Conclusion

When deciding between traditional models and neural networks, consider dataset size, problem complexity, interpretability.

Common types of neural networks are the following:

Popular deep learning libraries:

• TensorFlow: Google's deep learning framework for scalable machine learning;
• PyTorch: a flexible, dynamic deep learning library widely used in research and production.