Ready to unravel the mysteries of feedforward neural networks? This episode explores their architecture, the core of many AI systems. We break down the key components: input, hidden, and output layers and the computational neurons within. Discover how neurons perform weighted sums and apply activation functions, like sigmoid, tanh, ReLU, and softmax, introducing non-linearity crucial for complex modeling. Learn about weights and biases, which are parameters optimized to minimize errors. We’ll trace the flow of information, from input propagation to weighted summation, then activation and output generation. We’ll also use mathematical notation to visualize the computations of each layer. Join us to see how these networks process information step by step, transforming raw data into meaningful results!