My Journey into Deep Learning with fast.ai

After spending months researching various deep learning courses and reading countless reviews, I finally took the plunge into fast.ai. I wanted to share my experience and what I discovered about this unique approach to learning deep learning.

Why I Chose fast.ai

Before settling on fast.ai, I explored several other options - from theoretical university courses to various online platforms. What consistently came up in reviews and discussions was how fast.ai's "Practical Deep Learning for Coders" course takes a refreshingly different approach. Instead of front-loading theory and mathematics, it focuses on getting you to build working models quickly.

What really convinced me was reading experiences from actual students. Many mentioned how they went from complete beginners to building practical AI models in just a few weeks. The course requires:

  • Basic Python programming skills
  • High school math (you don't need to be a calculus expert!)
  • Curiosity and willingness to experiment

My Experience So Far (And What Others Say)

I'm several lessons in now, and I can validate what many reviewers mentioned - this course genuinely makes deep learning accessible. One review that really resonated with my experience said: "My main weakness is math, and as I follow Fast.ai, I find myself comfortable with learning both (Math and DL) on the way."

Here's what has surprised me most:

  • The practical-first approach actually works better than traditional theory-first methods
  • You start building impressive models before fully understanding the math (and that's okay!)
  • The community is incredibly supportive and active
  • The fastai library abstracts away complexity while still allowing you to "peek under the hood"

Common Questions Answered

After participating in the community and reading through forums, here are answers to questions I frequently see:

"Is the fastai library suitable for production?"

While the fastai library is excellent for learning and prototyping, many practitioners recommend transitioning to PyTorch or PyTorch Lightning for production systems. As one experienced user noted: "FastAI is great for learning but for most serious ML engineering, Pytorch Lightning is much better."

"Do I really need advanced math?"

Not to get started! The course brilliantly introduces concepts practically first, then gradually introduces the mathematical foundations as needed. You can learn the math alongside the practical implementation.

"What's the learning curve like?"

The initial curve is surprisingly gentle. You'll be building working models in your first few lessons. However, be prepared for more complexity as you dive deeper into customizing and optimizing your models.

What's Next on My Journey

Based on recommendations from the community, I'm planning to:

  • Complete the core fast.ai course
  • Gradually transition to PyTorch for more control over my models
  • Explore the Full Stack Deep Learning course from Berkeley (frequently recommended as a follow-up)
  • Build a portfolio of practical projects

Tips for Newcomers

From my research and experience so far:

  1. Don't get too attached to the fastai library - use it as a learning tool
  2. Join the fast.ai forums - the community is incredibly helpful
  3. Focus on building projects rather than perfect understanding initially
  4. Keep a learning journal (I wish I'd started this earlier!)

I'll be updating this blog as I progress further into the course and tackle more complex projects. Feel free to reach out if you're also starting this journey - I'd love to hear about your experience!

Resources I Found Helpful