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455 CARDS · 41 WEEKS · EVERY SOURCE THE PLAN NAMES

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Every video, chapter, paper and repository in the programme, as one card each, with the time it costs and why it is there. Filter by how long you have.

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Type Watch Read Run Track Biology AI & stats Build Craft Depth Core Optional Time ≤15 ≤30 ≤60 Week This week Clear all

5 cards shown. Stashes: LABLOOP-W00 Notebook (15) LABLOOP-W01 Notebook (18) LABLOOP-W02 Notebook (15) LABLOOP-W03 Notebook (19) LABLOOP-W04 Notebook (17) LABLOOP-W05 Notebook (16) LABLOOP-W06 Notebook (12) LABLOOP-W07 Notebook (12) LABLOOP-W08 Notebook (15) LABLOOP-W09 Notebook (10) LABLOOP-W10 Notebook (11) LABLOOP-W11 Notebook (12) LABLOOP-W12 Notebook (12) LABLOOP-W13 Notebook (13) LABLOOP-W14 Notebook (12) LABLOOP-W15 Notebook (10) LABLOOP-W16 Notebook (14) LABLOOP-W17 Notebook (13) LABLOOP-W18 Notebook (10) LABLOOP-W19 Notebook (10) LABLOOP-W20 Notebook (10) LABLOOP-W21 Notebook (13) LABLOOP-W22 Notebook (10) LABLOOP-W23 Notebook (9) LABLOOP-W24 Notebook (9) LABLOOP-W25 Notebook (12) LABLOOP-W26 Notebook (11) LABLOOP-W27 Notebook (10) LABLOOP-W28 Notebook (6) LABLOOP-W29 Notebook (7) LABLOOP-W30 Notebook (10) LABLOOP-W31 Notebook (8) LABLOOP-W32 Notebook (5) LABLOOP-W33 Notebook (13) LABLOOP-W34 Notebook (12) LABLOOP-W35 Notebook (10) LABLOOP-W36 Notebook (8) LABLOOP-W37 Notebook (7) LABLOOP-W38 Notebook (6) LABLOOP-W39 Notebook (6) LABLOOP-W40 Notebook (6)

10/14 Run 30 min W16 Build

huggingface/notebooks

Colab: examples/protein_language_modeling.ipynb — fine-tune/embed with ESM-2 through transformers; switch the model to facebook/esm2_t33_650M_UR50D in fp16 on your 3050

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In: LABLOOP-W16 Notebook

11/14 Run 30 min W16 Build

facebookresearch/esm

Read the README's "ESM-2" and embedding extraction sections; the original reference

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In: LABLOOP-W16 Notebook

12/14 Run 30 min W16 Build

evolutionaryscale/esm (now redirects to Biohub/esm)

Read only; the newer ESM C/ESM3 family. Parked for Half 2

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In: LABLOOP-W16 Notebook

13/14 Run 30 min W16 Build

aangelopoulos/conformal-prediction

Colab: notebooks/meps-cqr.ipynb (conformalized quantile regression on tabular data); map it onto your soft sensor

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In: LABLOOP-W16 Notebook

14/14 Run 30 min W16 Build

scikit-learn-contrib/MAPIE

Regression example with SplitConformalRegressor (or the current API); get coverage on held-out batches in 20 lines

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In: LABLOOP-W16 Notebook

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