Neural networks are structured in layers consisting of an input layer, hidden layers, and an output layer, mimicking the human brain to recognize patterns and address problems in AI and deep learning. Each node, or artificial neuron, functions like a linear regression model, where the connections between nodes have assigned weights reflecting their influence on the output. Data progresses through the network in a feed-forward manner. An example decision-making process is illustrated through a surfing scenario, where factors like wave quality, crowd presence, and safety are weighted to predict the outcome of going surfing. Through mathematical evaluation, the weighted inputs are processed to yield a final output. Neural networks improve over time using training data, employing supervised learning on labeled datasets. Training focuses on minimizing a cost function to ensure accuracy as the model iteratively adjusts its weights and biases, utilizing gradient descent to fit the training data effectively.