From Curiosity to Commitment: Stepping into AI

Artificial Intelligence has quietly seeped into the fabric of our lives. It’s the ghost in the machine that recommends our next song, the silent navigator that guides our commute, and the unseen force shaping conversations about our future. To the outside eye, it often operates like a form of modern magic.

For a long time, I've been a spectator, fascinated by its power but mystified by its methods. But I am convinced that behind this curtain of magic lies not spells, but science; not incantations, but engineering. And I have decided it’s time to trade my spectator’s seat for an apprentice’s workbench.

This post marks that commitment. It’s the start of a public journey to learn Deep Learning from the ground up, documenting the process step by step.

The Method

My initial roadmap will be charted by the acclaimed Deep Learning lecture series from Professor Mitesh Khapra. His work comes highly recommended, and I'm ready to dive in. My plan is to learn actively, guided by the simple principle that to truly understand something, you must try to explain it.

Here, in this space, I will document my attempts to:

  • Distill the core concepts from each lecture.

  • Translate the dense mathematics and code into plain, intuitive English.

  • Capture those precious 'aha!' moments when a complex idea finally clicks into place.

Crucially, this will be an honest account. I will be just as transparent about the concepts that leave me stumped as I am about the ones I grasp.

An Open Invitation

This isn’t intended to be a monologue; it's an invitation to a conversation. This is a space for fellow beginners to find a study companion, for the curious to see what the learning curve actually looks like, and hopefully, for seasoned experts to share their wisdom and correct a misstep or two along the way. All perspectives are welcome.

But before we can write a single line of code, we need to understand the ground we stand on. Before we look forward, we must first look back.

The story of AI is not a simple, linear path. It's a dramatic saga of brilliant breakthroughs and bitter rivalries, of golden ages of discovery and decades-long "AI Winters" of disillusionment. Why did a field with so much promise suddenly freeze over for nearly twenty years? And what, surprisingly, can the brain of a cat from 1959 teach us about building the thinking machines of today?

That’s the incredible story we’ll uncover in the next post, as we lay our foundation by exploring the rich, and often surprising, history of this field.

The journey begins there.

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