Ahmad shares a self-study roadmap for deep learning broken into 5 phases, outlining hands-on milestones that learners should aim to hit. These include building an autograd engine from scratch, writing a mini-GPT from scratch, implementing LoRA and fine-tuning a model on real data, and — humorously — hating CUDA at least once and crying along the way. The advice is practical and self-paced: skip what you already know, rewatch what confuses you, and keep going when stuck. The post captures the gritty, iterative nature of learning ML engineering with a relatable, no-nonsense tone.