Your Roadmap
What to do next.
Think back to your first morning at Neuron Labs, and the word tensor on the whiteboard. Since then, you've built a digit reader, a C N N, a G P T, a G A N, and a diffusion model, from scratch. Very few people have done that. Let's make sure you keep going.
- Remember 'tensor' on the whiteboard?
- You've built a GPT from scratch
- Very few people have
Three myths to ignore
Three last myths. People say that you need a PhD. You don't, because you've just built the core ideas yourself. People say that you need huge compute. You don't, as transfer learning runs on a laptop. And people say that neural networks are magic. By now, you know that it's just multiplication, addition, and small steps downhill.
Myth
- You need a PhD
- You need huge compute
- Neural networks are magic
Reality
- You built the core yourself
- Transfer learning needs a laptop
- Multiply, add, step downhill
The next ninety days
In the next thirty days, rebuild Tiny GPT with no help. By sixty days, fine tune a pretrained model on your own images, and share it on GitHub. By ninety days, read one famous paper a week, and deploy one model that a stranger can use.
- 30 daysrebuild TinyGPT alone
- 45 daysyour own image dataset
- 60 daysshare it on GitHub
- 75 daysread one paper a week
- 90 daysdeploy a live model
Where to go next
From here, there are clear paths. Natural Language Processing goes deep into language. Computer Vision masters images and video. Agentic A I teaches models to act. And M L Ops teaches you how to run it all at scale. All of these are in this series.
Summary
Thank you for spending these hours with me. Deep learning isn't magic. It's simple ideas, stacked up, trained with patience. Just like you, in this course. One small step at a time, you've become someone who can build intelligent systems. Now go and build one.
Key takeaways- Simple ideas, stacked up
- Build something this week
- Keep learning in public