Explore the foundations, architectures, and real-world applications of neural networks that are reshaping artificial intelligence. From perceptrons to transformers, master the math, code, and theory behind modern AI.
A comprehensive breakdown of how gradients flow backward through networks, chain rule applications, and common optimization pitfalls in modern deep learning frameworks.
Exploring the evolution of transformer models, mixture-of-experts routing, kv-cache optimization, and how sparse attention mechanisms scale to trillion-parameter systems.
An accessible introduction to how convolutional layers extract spatial hierarchies, pooling strategies, and building your first image classifier from scratch.
Deep dive into the min-max game of GANs, mode collapse diagnostics, Wasserstein distance, and practical tips for training stable generative models.
Analyzing the Chinchilla paper insights, power-law scaling relationships, and how dataset curation intersects with model size for optimal performance.
How to audit training datasets for demographic skew, implement fairness constraints during optimization, and navigate the growing field of AI alignment.