Just the catch-up I needed I attended Deep Atlas because I felt like the software industry has started to go in a new direction, and I'd noticed conversations about ML and AI happening at work that I wasn't able to keep up with. Coming away from the class after two weeks, I felt ready to engage with a whole range of ML and AI topics. The class delivered on its promises for me, and I feel capable of both practical ML engineering and fighting my way through AI white-papers (e.g. "Attention Is All You Need") to learn more. Hugging Face now feels more like a useful library of tools than a pile of enigmatic repos! Deep Atlas was a big fast-forward for my learning. I might have eventually gotten there on my own, but it's hard to say how long (and how much effort) that might have taken. Learning everything in a condensed period was effective and honestly quite fun!

The ML Bootcamp I was looking for I learned practical skills that allow me to jumpstart a ML career. After this course I'm able to have conversations with Data Scientists and am confidently working on a side project with my peers.

A masterclass in distillation Neural networks, fine tuning, hyper parameters, transformers. These were some of the words flying around my feed that I couldn't quite grasp. I tried to learn them on my own, but then I uncovered even more arcane words: vectorization, embeddings, latent space, weights, gradient descent. Deep Atlas put together a masterclass in distillation. From linear algebra to transformers (the architecture that powers LLMs today), there was a crystal clear roadmap, paved with engaging lectures and solidifying real world projects. After the class, I feel ahead of this incoming wave. I’m well equipped to integrate AI into both my work and personal projects, and to continue my self-study at the cutting edge of this rapidly evolving industry.