Cheat sheet · Specialise
Machine learning cheat sheet
Data, models, deep learning.
Chapters
- 1Python for data
- 2The maths you need
- Data cleaning and feature engineering
+ 5 more
- 8 chapters
- 69 concepts
Machine learning cheat sheets on Sefism, written by Tauseef Fayyaz: every concept in plain words, why it is used and the pitfall that catches people out.
Reading is free. Adding a roadmap, keeping a cheat sheet or asking Tauseef a question uses credits, which only make sure the platform goes to people who are actually learning.
Go deep in one area of engineering.
Cheat sheet · Specialise
Data, models, deep learning.
Chapters
+ 5 more
Cheat sheet · Specialise
Statistics, pandas, ML, experiments.
Chapters
+ 7 more
Cheat sheet · Specialise
Ship, serve and monitor models.
Chapters
+ 7 more
Cheat sheet · Specialise
Tensors, autograd, nn.Module, training loops, data loading, GPUs, mixed precision, torch.compile, DDP and export in PyTorch.
Chapters
+ 6 more
Cheat sheet · Specialise
The Hub, transformers, tokenisers, datasets, fine-tuning, LoRA, quantisation, inference and embeddings with the Hugging Face libraries.
Chapters
+ 5 more
Cheat sheet · Specialise
Experiment tracking, the tracking server, models and signatures, the Model Registry with aliases, serving, evaluation, GenAI tracing and MLOps.
Chapters
+ 6 more
Each of these is its own page: the foundations shelf, the career shelf, the sheets that are open end to end. The filters above do the same thing without a page load.
Writing only gets you so far. If you want an answer to your situation rather than the general case, book a session and we will work through it together. Sessions are free for approved Sefism members, and a few slots open each week.
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