『The Practical AI Digest』のカバーアート

The Practical AI Digest

The Practical AI Digest

著者： Mo Bhuiyan via NotebookLM
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 Distilling AI/ML theory into practical insights. One concept at a time. No jargon.Mo Bhuiyan via NotebookLM
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  • Graph Neural Networks: Learning from Connections, Not Just Data

    Graph Neural Networks: Learning from Connections, Not Just Data
    2025/09/30

    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.
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    31 分
  • Neuro-Symbolic AI: Combining Learning With Logic

    Neuro-Symbolic AI: Combining Learning With Logic
    2025/09/16

    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.
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    25 分
  • LLMs in Chip Design: How AI Is Entering the Hardware Workflow

    LLMs in Chip Design: How AI Is Entering the Hardware Workflow
    2025/09/02

    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.
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    20 分
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