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You sit through the workshops and the demos. Someone shows a clever prompt, everyone marvels at the output, and no one stops to ask what the model is actually doing - what data it is seeing, what task it is solving, why it works here but might not on the next example. So when the next model drops, you feel like you have to relearn everything. I call this prompt and pray, and it wears you out.
The fix is boring, and it lasts. Before reaching for a model, ask three questions: what data do I have, what task am I doing, which method fits. My PhD supervisor put it in three words - "Let's start with the task" - and years of working through data types and tasks showed me he was right. That is what this book teaches: how to see AI through the lens of data types and tasks.
Why it matters now, in the age of LLMs and agents: prompts are brittle, and a framework for thinking is more durable than a harness. AI has always been about data and tasks. LLMs wrap over those tasks in a more general way; agents are an LLM orchestrating tools and other LLMs on tasks. Strip the headline away and the data types and tasks are the same ones that were there ten years ago.
What you get: the book works through the main data types - tabular, text, image, networks, and time series - the same way each time. What the data is, how you break it down, how AI works on it. From the current landscape to the frontier. Learn the pattern once and you can look at any new tool or paper and understand what it is doing, instead of relearning the hype every six months.
If you are here for the frontier, you are in the wrong place. This is deliberately about the boring fundamentals - the part of AI that does not expire when the next model drops. You get the PDF to download and keep.
You sit through the workshops and the demos. Someone shows a clever prompt, everyone marvels at the output, and no one stops to ask what the model is actually doing - what data it is seeing, what task it is solving, why it works here but might not on the next example. So when the next model drops, you feel like you have to relearn everything. I call this prompt and pray, and it wears you out.
The fix is boring, and it lasts. Before reaching for a model, ask three questions: what data do I have, what task am I doing, which method fits. My PhD supervisor put it in three words - "Let's start with the task" - and years of working through data types and tasks showed me he was right. That is what this book teaches: how to see AI through the lens of data types and tasks.
Why it matters now, in the age of LLMs and agents: prompts are brittle, and a framework for thinking is more durable than a harness. AI has always been about data and tasks. LLMs wrap over those tasks in a more general way; agents are an LLM orchestrating tools and other LLMs on tasks. Strip the headline away and the data types and tasks are the same ones that were there ten years ago.
What you get: the book works through the main data types - tabular, text, image, networks, and time series - the same way each time. What the data is, how you break it down, how AI works on it. From the current landscape to the frontier. Learn the pattern once and you can look at any new tool or paper and understand what it is doing, instead of relearning the hype every six months.
If you are here for the frontier, you are in the wrong place. This is deliberately about the boring fundamentals - the part of AI that does not expire when the next model drops. You get the PDF to download and keep.