This page explores methods for visualizing the internal workings of neural networks during both training and inference phases. It provides insights into how hidden layers, activation patterns, and weight distributions evolve over time, offering a clearer understanding of model behavior. The content is part of a broader educational effort to demystify deep learning concepts through practical examples.
The resource includes full code examples and references to a comprehensive book on building neural networks from scratch. Additional support and community engagement are available through various platforms including Discord, Reddit, Twitter, and Instagram. The project is designed to help learners grasp complex AI mechanisms by observing them in real time, with transparent access to materials and ongoing updates.