In this episode, we dive into the Model Context Protocol, or MCP. It’s a new standard that helps large language models connect with real-world tools, data, and APIs in a more structured way. We’ll break down how MCP works, why it matters for building smarter AI agents, and what it means for developers working on enterprise-grade AI systems.

This episode breaks down how GANs work by starting with deep learning basics like CNNs, gradient descent, and regularization. We then get into what actually goes wrong when training these models and how to deal with it. It’s practical, straightforward, and meant for anyone trying to make sense of GANs in the real world.

In this episode, we unpack how Bayesian and Frequentist statistical approaches tackle marketing performance analysis, focusing on Marketing Mix Modeling (MMM). You’ll learn the key differences in interpretation, how Bayesian methods enable sequential updates and uncertainty modeling, and why they’re gaining traction in modern marketing analytics. Ideal for marketers, data scientists, and anyone curious about the “why” behind the math.