Collection: Neural Networks

Neural networks involve a combination of linear algebra, calculus, and optimization theory. At their core, these networks consist of interconnected layers of artificial neurons, each performing weighted sum operations followed by non-linear activation functions. The learning process relies on backpropagation, which uses the chain rule of calculus to compute gradients and update network parameters. Optimization algorithms like stochastic gradient descent are employed to minimize a loss function, adjusting the network's weights and biases. Additionally, concepts from probability theory and statistics play a role in understanding network behavior and performance.