Neural networks consist of node layers including an input layer, hidden layer, and output layer, mimicking the human brain to recognize patterns and solve problems in AI and deep learning. Each node functions as a linear regression model, where connections between nodes have weights that determine their influence on outputs. Data flows through the network in a feed forward manner, with predictions based on input data and assigned weights. For instance, deciding whether to go surfing involves evaluating factors like wave quality, crowd size, and safety from sharks, each assigned a weight based on importance. By plugging in these values into a formula, the output is determined. Neural networks use training data to enhance accuracy over time through supervised learning on labeled datasets. Evaluating model accuracy involves a cost function, which the model attempts to minimize by adjusting weights and biases during training via gradient descent, thereby improving performance on the dataset.