The Practical AI Digest

Mo Bhuiyan via NotebookLM

Distilling AI/ML theory into practical insights. One concept at a time. No jargon.

Episodes

  1. FEB 3

    Efficient Fine-Tuning: Adapting Large Models on a Budget

    This episode dives into strategies for fine-tuning gigantic AI models without needing massive compute. We explain parameter-efficient fine-tuning methods like LoRA (Low-Rank Adaptation), which freezes the original model and trains only small adapter weights, and QLoRA, which goes a step further by quantizing model parameters to 4-bit precision. You’ll learn why techniques like these have become essential for customizing large language models on modest hardware, how they preserve full performance, and what recent results (like fine-tuning a 65B model on a single GPU) mean for practitioners.

    29 min

  2. JAN 20

    Diffusion Models: AI Image Generation Through Noise

    In this episode, we break down what diffusion models are and why they’ve become the go-to method for AI image generation. You’ll learn how these models gradually add and remove noise to transform random pixels into coherent images, enabling use cases from art creation to image restoration. We also explore recent advances like latent diffusion, which compresses the generation process for efficiency, and discuss how diffusion techniques have achieved state-of-the-art results in text-to-image tasks while remaining flexible for control and guidance.

    25 min

  3. 09/30/2025

    Graph Neural Networks: Learning from Connections, Not Just Data

    This episode breaks down what graph neural networks (GNNs) are and why they matter. You’ll learn how GNNs use nodes and edges to represent relationships and how message passing lets models make sense of social, biological, and networked data. We’ll also cover recent advancements like PGNN for multi-modal graphs and common pitfalls like scalability and over-smoothing.

    31 min

  4. 09/16/2025

    Neuro-Symbolic AI: Combining Learning With Logic

    In this episode, we explain what neuro-symbolic AI is and why it matters. You’ll learn how neural networks handle patterns, how symbolic systems handle rules, and how combining the two can help models reason more reliably. We also cover real examples where this approach is already being applied in assistants and robotics, showing how it could make AI systems more trustworthy and useful.

    25 min

  5. 09/02/2025

    LLMs in Chip Design: How AI Is Entering the Hardware Workflow

    In this episode, we look at how large language models are being used in chip and hardware design. We break down what LLM-aided design actually means, how models can generate HDL code, assist with testbench creation, and even support formal verification. You'll also hear about real-world tools like ChatEDA and how companies are starting to use AI in their silicon development workflows.

    20 min

  6. 08/19/2025

    How Embeddings and Vector Databases Power Generative AI

    This episode explains how embedding models turn language into numerical vectors and how vector databases like Pinecone, FAISS, or Weaviate store and search those vectors efficiently. You'll learn how this system enables GenAI models to retrieve relevant information in real-time, power RAG pipelines, and scale up tool-augmented LLM workflows.

    19 min

  7. 08/05/2025

    Agentic AI: What Happens When Models Start Acting

    In this episode, we explore agentic AI systems built to not just predict or classify, but to plan, reason, and act autonomously. We break down what makes these models different, how they use tools, memory, and feedback to complete tasks, and why they represent the next step beyond traditional LLMs. You’ll hear how concepts like action loops, world modeling, and autonomous decision-making are shaping everything from research tools to enterprise automation.

    19 min

  8. 07/22/2025

    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 what makes it different from older architectures like RNNs or CNNs. You’ll walk away with an intuitive grasp of key terms like query, key, value, and how attention layers help with context handling in language, vision, and beyond.

    20 min

  9. 07/08/2025

    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, and how random sampling helps model uncertainty. We also explore gradient-based techniques like Hamiltonian Monte Carlo, highlighting their role in modern statistical modeling. Ideal for anyone curious about the mechanics behind simulations and complex probabilistic models.

    24 min

  10. 06/24/2025

    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 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.

    17 min

  11. 06/10/2025

    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 how to deal with it. It’s practical, straightforward, and meant for anyone trying to make sense of GANs in the real world.

    28 min

  12. 05/27/2025

    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 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.

    23 min
  • 12 Episodes

About

Distilling AI/ML theory into practical insights. One concept at a time. No jargon.

Information

  • Creator
    Mo Bhuiyan via NotebookLM
  • Years Active
    2025 - 2026
  • Episodes
    12
  • Rating
    Clean
  • Copyright
    © Mo Bhuiyan via NotebookLM
  • Show Website
    The Practical AI Digest