👉 Neural Networks, or "nn" as shortcut, are computational models inspired by the human brain's neural networks, used extensively in machine learning and deep learning. They consist of layers of interconnected nodes or "neurons" that process information through weighted connections and activation functions. Each neuron receives input, multiplies it by weights, sums the outputs, applies an activation function to introduce non-linearity, and passes the result to the next layer. This architecture allows neural networks to learn complex patterns in data by adjusting these weights during training, typically using backpropagation and gradient descent. The "math" in nn revolves around linear algebra (matrix operations for weight and output calculations), calculus (for optimizing weights via gradients), and probability theory (for handling uncertainty in predictions). Together, these mathematical tools enable neural networks to model intricate relationships in data, powering applications from image recognition to natural language processing.
