Discover the fascinating world of neural networks in this episode of How AI Works. Host Daniel Cole explains how these digital brain-like structures process information, learn from data, and power modern AI applications. Learn about the fundamental architecture of neural networks, from input layers to hidden layers, and understand how backpropagation enables machine learning. Explore different types of networks including convolutional neural networks for image processing, recurrent networks for sequential data, and transformer architectures that drive large language models. The episode covers the training process, feature learning, and the massive scale of contemporary AI systems with billions of parameters. Cole discusses both the remarkable capabilities and important limitations of neural networks, emphasizing that despite their biological inspiration, these systems process information very differently from human brains. Perfect for anyone curious about artificial intelligence, machine learning fundamentals, and the technology behind image recognition, language translation, and autonomous systems. Gain insights into pattern recognition, data processing, and the computational requirements of training large-scale neural networks in today's AI landscape.