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.

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!

My DeepAtlas experience I wanted to learn about ML because I was seeing it emerge in both my current job and my side interests and everything seemed to be speeding towards AI features. I'm not sure I would have been able to learn it on my own as I'm the type of person who benefits a lot from course structure and to really understand the core concepts I needed to test my understanding by discussing with others. Having a group of other highly motivated and talented individuals made the pace and the difficult topic so much more manageable, I don't think I could have done it without such an amazing community of students and instructors. I started the course with very basic understanding of neural networks but no understanding of the linear algebra or modern use cases of ML. I now have the skills needed to complete a passion project of mine that before I couldn't even imagine the pieces that needed to be done. Also I have the knowledge now to keep up and understand the AI team at my current company and how they are solving issues or techniques they use for building features. ML is going to become such a valuable skill that will be needed by every engineer and data scientist in the future so I'm thankful I had the opportunity to get ahead of the curve and start my journey into ML now.