The key components of a neural net include the input layer, hidden layers, output layer, activation functions, and connection weights. The input layer receives the initial data that is processed by the neural net. The hidden layers perform computations and apply activation functions to produce intermediate outputs. The output layer provides the final result or prediction. Activation functions introduce nonlinearity, enabling the neural net to model complex relationships. Connection weights represent the strength of the connections between neurons, influencing the flow of information during computation.