In this comprehensive webinar, you'll gain a thorough understanding of how neural networks learn from the ground up. We will start by demystifying what neural networks really are, setting a solid foundation for beginners and enthusiasts alike. Dive deep into the architecture of these data structures that are modeled after brains as we dissect the structure of neural nets, revealing how layers of mathematics and data processing simulate the learning process. You'll witness the magic of feedforward computation, where the input is transformed step-by-step into an output that can approximate any function with astonishing accuracy. We'll unravel the complexities of backpropagation — the method by which neural networks learn from their mistakes. Discover how subtle adjustments in their digital synapses enable them to improve over time, much like the human brain.

Whether you're a budding data scientist, an AI enthusiast, or simply curious about the inner workings of machine learning, this webinar is your stepping stone to a deeper appreciation of the technological marvels behind language models. This is part 1 of a 4 part series.