Welcome to an introduction to neural networks. A neural network is a computational model inspired by the structure and function of the human brain. It consists of interconnected nodes, called neurons, organized in layers. As you can see in this diagram, a typical neural network has three main components: an input layer that receives data, one or more hidden layers that process the information, and an output layer that produces the final result. The connections between neurons represent weights that are adjusted during training, allowing the network to learn patterns from data.
Let's look at how a single neuron works in a neural network. A neuron is the basic computational unit that processes information. First, it receives multiple inputs, each with an associated weight that determines its importance. The neuron computes a weighted sum of these inputs, adding a bias term. Then, it applies an activation function to introduce non-linearity. Common activation functions include sigmoid, shown here, ReLU, and tanh. The activation function determines whether and to what extent the neuron should fire or pass information to the next layer. This simple mechanism, when repeated across many neurons in multiple layers, enables neural networks to learn complex patterns and relationships.
Now, let's explore how neural networks learn through the training process. Training involves several key steps. First, in forward propagation, input data flows through the network to generate predictions. Then, we calculate the loss, which measures the difference between the network's predictions and the ground truth. Next comes backpropagation, where the error is propagated backward through the network to compute gradients. These gradients indicate how each weight contributed to the error. Finally, an optimization algorithm like Stochastic Gradient Descent updates the weights to minimize the loss. This entire process is repeated many times with different training examples until the network converges to a solution that generalizes well to new, unseen data.