A Densa Rede Neural (DNN) is a foundational architecture in the campo de inteligência artificial and machine learning, particularly within the realm of deep learning. In a DNN, every neuron in a given layer is connected to all neurons in the subsequent layer, creating a rede totalmente conectada structure. This characteristic allows the model to learn complex patterns and relationships in data.
Dense Neural Networks typically consist of an input layer, one or more hidden layers, and an output layer. The input layer receives the input data, while the hidden layers perform computations and transformations of that data through weighted connections and funções de ativação. The output layer delivers the final predictions or classifications based on the processed information.
Os componentes principais das Redes Neurais Densas incluem:
- Funções de Ativação: Non-linear functions applied to the output of neurons, enabling the network to learn complex patterns. Common activation functions include ReLU (Rectified Linear Unit), Sigmoide, and Tanh.
- Pesos e Biases: Parâmetros adjusted during training to minimize the difference between predicted and actual outcomes. Weights determine the strength of connections between neurons, while biases allow adjustment of the output.
- Retropropagação: A training algorithm that updates the weights and biases based on the error of the output. This process involves propagating the error backward through the network to optimize the model.
Dense Neural Networks are widely used in various applications, including image recognition, processamento de linguagem natural, and speech recognition. Their ability to model intricate relationships makes them a powerful tool for solving complex problems in diverse domains.