A compact 4-page Deep Learning reference covering neural networks, CNNs, Transformers, LLMs, fine-tuning, generative AI, optimization, scaling, and practical PyTorch essentials.
One resource. A new possibility. Personal-use license.
A SMALL STEP. A BIG DIFFERENCE.
A 4-page Deep Learning Cheatsheet built for 2026, taking you from fundamental neural networks to modern LLM and generative-AI systems. It covers core formulas, optimization, CNNs, Transformers, attention variants, MoE, scaling laws, memory and parallelism, LoRA/QLoRA, alignment methods, inference, diffusion and flow matching, evaluation, debugging, and practical PyTorch defaults—all designed for fast revision, interviews, study, and day-to-day AI/ML work.
I created this cheatsheet to put the most useful Deep Learning formulas, architectures, LLM concepts, training techniques, and practical defaults into one quick-reference resource.
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