A Neural Network consists of layers of interconnected nodes, or “neurons”, each of which takes in input, performs a computation on that input, and passes the output to the next layer. The input layer receives the raw data, the output layer produces the final result, and the layers in between, known as hidden layers, perform computations that gradually transform the input into the output.

Each neuron in a layer receives input from every neuron in the previous layer. These inputs are multiplied by weights, which determine the importance of the input in the computation. The weighted inputs are then summed and passed through an activation function, which determines the neuron’s output.

Neural Networks are trained using a process called backpropagation, which involves adjusting the weights based on the difference between the network’s output and the desired output. This process is repeated many times on a set of training data, gradually improving the network’s accuracy.

Neural Networks can have many layers (making them “deep”) and many neurons per layer, allowing them to learn complex patterns in large amounts of data. However, they require significant computational resources to train and can be difficult to interpret due to their complexity.

Input Layer: The input layer receives the raw data or features and passes them to the subsequent layers. The number of nodes in the input layer corresponds to the dimensionality of the input data.

Hidden Layers: Hidden layers perform transformations on the data. Each node in a hidden layer is a linear combination of the outputs of the nodes from the previous layer, modulated by the weights of the connections. This linear combination is then passed through an activation function, such as the sigmoid, hyperbolic tangent, or Rectified Linear Unit (ReLU), which introduces non-linearity into the model.

Output Layer: The output layer produces the predictions or classifications of the network. The activation function in the output layer is chosen based on the nature of the problem, such as a softmax function for multi-class classification or a linear function for regression.

During the training phase, the Neural Network uses an algorithm, typically some variant of stochastic gradient descent, to iteratively adjust the weights of the connections to minimize a loss function. The loss function quantifies the discrepancy between the network’s predictions and the actual target values.