Neural networks form the basis of deep learning, a powerful technique for solving complex problems. They are designed to mimic the human brain, with interconnected nodes, called neurons, that process and transmit information. Activation functions play a crucial role in neural networks as they determine the output of each neuron. Some commonly used activation functions include sigmoid, tanh, and ReLU.

The feedforward process is the core component of neural networks. It involves taking input data, multiplying it by corresponding weights, and applying an activation function to obtain the output. This output is then passed to the next layer, forming a sequential flow of information. The backpropagation process, on the other hand, is responsible for updating the weights of the neural network based on the error in the output, using techniques like gradient descent to minimize the loss function.

To better understand these concepts, let's consider a basic example. Imagine you want to predict whether a student will pass or fail based on their hours of study and hours of sleep. By feeding the neural network with historical data of students including these features as inputs and their corresponding pass/fail outcome as the output, the network can learn to make predictions on new, unseen data.

With a solid understanding of the fundamentals, you're ready to dive deeper into the fascinating world of neural networks and unleash their potential in solving complex problems!