In this episode, we open the hood of the machine. Having established that language modeling is a probability game, we now examine the actual computational structures that make learning possible. We trace the architectural evolution from simple layered networks to the breakthrough that powers modern AI: Self-Attention.
Join us as we:
• Build the Basics: We explain the fundamental components of neural networks—linear layers, nonlinear activation functions (like ReLU and GELU), and embeddings—that transform discrete tokens into rich vector representations.
• Trace the History: We follow the progression from rigid Feedforward Networks to Recurrent Neural Networks (RNNs), analyzing why earlier systems struggled with memory and long-range dependencies.
• Reveal the Game Changer: We introduce Self-Attention, the mechanism that replaced sequential processing with parallel interaction, allowing models to "see" the entire context at once.
• Optimize the Learning: We touch on how billions of parameters are actually adjusted using Gradient Descent and backpropagation to minimize error and "learn" language patterns.
This episode bridges the gap between statistical theory and the specific architecture—the Transformer—that we will dismantle in the next episode.
