Understanding Attention: Why Transformers Actually Work
This episode unpacks the attention mechanism at the heart of Transformer models. We explain how self-attention helps models weigh different parts of the input, how it scales in multi-head form, and wh...
22 Jul 202520min
Markov Chains, Monte Carlo, and HMC: A Deep Dive
In this episode, we break down the essentials of Markov Chains, Monte Carlo simulations, and Markov Chain Monte Carlo methods. We explain key ideas like memoryless processes, stationary distributions,...
8 Jul 202523min
The Model Context Protocol (MCP): Making LLMs Actually Useful
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 bre...
24 Jun 202516min
Generative Adversarial Networks (GANs) Explained: From DL Basics to Real-World Training Tips
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 h...
10 Jun 202527min
Bayesian vs. Frequentist Thinking in Marketing Mix Modeling
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 inte...
27 Mai 202522min
