FOLLOW THE EVIDENCE

Keep the sources
in sight.

The papers, documentation, courses and practitioner writing cited in this edition, with the pages that use each link. Listing a source does not endorse every claim in it. The edition date does not mean each source was independently checked on that date.

354 sources
Download the inventory as JSON ↓
Building with the Claude API (Claude Academy) ↗
https://academy.claude.com/courses/building-with-the-claude-api
Used in 2 pages
EMNLP 2024 RAG/long-context study ↗
https://aclanthology.org/2024.emnlp-industry.66/
Used in 2 pages
open standard ↗
https://agentskills.io/home
Used in 1 page
Criteo corrected uplift dataset ↗
https://ailab.criteo.com/criteo-uplift-prediction-dataset/
Used in 2 pages
Think Python, 3rd edition (2024) ↗
https://allendowney.github.io/ThinkPython/
Used in 1 page
UCI Online Retail ↗
https://archive.ics.uci.edu/dataset/352/online+retail
Used in 3 pages
UCI Iranian Churn ↗
https://archive.ics.uci.edu/dataset/563/iranian+churn+dataset
Used in 2 pages
Dudík, Langford and Li — Doubly Robust Policy Evaluation and Learning ↗
https://arxiv.org/abs/1103.4601
Used in 4 pages
Dynamic pricing with limited supply ↗
https://arxiv.org/abs/1108.4142
Used in 1 page
Wager and Athey's causal-forest paper ↗
https://arxiv.org/abs/1510.04342
Used in 1 page
Athey and Wager — Policy Learning with Observational Data ↗
https://arxiv.org/abs/1702.02896
Used in 2 pages
Künzel et al. — Meta-learners for estimating heterogeneous treatment effects ↗
https://arxiv.org/abs/1706.03461
Used in 2 pages
_Attention Is All You Need_ ↗
https://arxiv.org/abs/1706.03762
Used in 2 pages
t - lookback, t)`, using records available by `t`. Build labels from the outcome window that follows. An unresolved outcome is unknown, not a negative. Record `label_available_at` and `outcome_observed` separately from the planned maturity date, because a window can pass while its source ledger is still incomplete. At a simulated training cutoff, use only labels that the observation window, reporting delay and actual availability allow. Report missing outcomes by cohort and exclusion reason. Elapsed time alone does not prove follow-up is complete. Test unique keys, join row counts, timezone conversion, missingness, late arrivals and duplicate events. Store the extraction cutoff and source version with the data. Record permission to use the data, keep customer identifiers out of public examples, and publish only data whose licence and permissions allow it. When the original data is private, a reproducible synthetic example is enough. Reproduction needs a data manifest. Record the source URL or export/query version, licence, retrieval date, extraction cutoff, raw-file checksum, schema version, code commit and environment. Where permitted, keep raw inputs unchanged. Document any sampling, seeds and exclusions, and use the same population denominator in your text and charts. Run the cleaning and analysis in a fresh process with the locked environment, not from the notebook's execution history. The questions in [Datasheets for Datasets ↗
https://arxiv.org/abs/1803.09010
Used in 2 pages
Time-uniform, nonparametric, nonasymptotic confidence sequences ↗
https://arxiv.org/abs/1810.08240
Used in 1 page
Hadad et al. (PNAS, 2021; author preprint) ↗
https://arxiv.org/abs/1911.02768
Used in 2 pages
Bojinov, Simchi-Levi and Zhao — Design and Analysis of Switchback Experiments ↗
https://arxiv.org/abs/2009.00148
Used in 2 pages
LoRA: Low-Rank Adaptation of Large Language Models ↗
https://arxiv.org/abs/2106.09685
Used in 1 page
split conformal prediction ↗
https://arxiv.org/abs/2107.07511
Used in 2 pages
PagedAttention ↗
https://arxiv.org/abs/2309.06180
Used in 1 page
AgentDojo benchmark ↗
https://arxiv.org/abs/2406.13352
Used in 2 pages
DeepSeek-R1 paper ↗
https://arxiv.org/abs/2501.12948
Used in 2 pages
NoLiMa (2025) ↗
https://arxiv.org/abs/2502.05167
Used in 2 pages
MMTEB (2025) ↗
https://arxiv.org/abs/2502.13595
Used in 2 pages
CausalPFN ↗
https://arxiv.org/abs/2506.07918
Used in 2 pages
Chronos-2 ↗
https://arxiv.org/abs/2510.15821
Used in 2 pages
TabICLv2: A better, faster, scalable, and open tabular foundation model ↗
https://arxiv.org/abs/2602.11139
Used in 1 page
arXiv version ↗
https://arxiv.org/abs/2602.16666
Used in 2 pages
March arXiv version ↗
https://arxiv.org/abs/2603.05451
Used in 2 pages
CIPS/CDR — Ranking evaluation from deterministic slates ↗
https://arxiv.org/abs/2603.21485
Used in 2 pages
Causal Foundation Models with Continuous Treatments — CCPFN ↗
https://arxiv.org/abs/2605.15133
Used in 2 pages
SurvPFN — Towards Foundation Models for Survival Predictions ↗
https://arxiv.org/abs/2606.04564
Used in 2 pages
Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED ↗
https://arxiv.org/abs/2606.18750
Used in 3 pages
BeyondArena preprint ↗
https://arxiv.org/abs/2606.30410
Used in 3 pages
Zheng and Jin — Prediction Sets for Counterfactual Decisions ↗
https://arxiv.org/abs/2607.02206
Used in 2 pages
preprint revised on 4 October 2026 ↗
https://arxiv.org/abs/2607.08849v2
Used in 2 pages
UpliftBench ↗
https://arxiv.org/abs/2608.00915
Used in 2 pages
Lee et al. — When Offline Evaluation Misleads ↗
https://arxiv.org/abs/2608.11560
Used in 2 pages
2026 linguistic-confidence study ↗
https://arxiv.org/abs/2608.28382
Used in 2 pages
TabPFN-3.5 report ↗
https://arxiv.org/abs/2609.17895
Used in 3 pages
UNIQUE ↗
https://arxiv.org/abs/2609.23718
Used in 2 pages
Zhang, Wang and Luo — Optimal Nonparametric Dynamic Pricing with Censored Demand and Adversarial Inventory ↗
https://arxiv.org/abs/2609.32949
Used in 2 pages
September 2026 Mid-Harness preprint ↗
https://arxiv.org/abs/2609.39982
Used in 2 pages
full paper ↗
https://arxiv.org/html/2606.30410v1
Used in 1 page
Automate the Boring Stuff with Python, 3rd edition ↗
https://automatetheboringstuff.com/3e/
Used in 1 page
CatBoost's categorical handling ↗
https://catboost.ai/docs/en/
Used in 1 page
categorical-feature support ↗
https://catboost.ai/docs/en/features/categorical-features
Used in 4 pages
author's release announcement ↗
https://causalinf.substack.com/p/online-version-of-the-remix-is-now
Used in 1 page
Interpretable Machine Learning ↗
https://christophm.github.io/interpretable-ml-book/
Used in 2 pages
VS Code Data Science tutorial (Python + Jupyter extensions) ↗
https://code.visualstudio.com/docs/datascience/data-science-tutorial
Used in 1 page
Google Colab ↗
https://colab.research.google.com/
Used in 1 page
IBM Telco sample documentation ↗
https://community.ibm.com/community/user/blogs/steven-macko/2019/07/11/telco-customer-churn-1113
Used in 2 pages
Practical Deep Learning for Coders ↗
https://course.fast.ai/
Used in 2 pages
CS229 Machine Learning — main lecture notes (publicly downloadable PDF) ↗
https://cs229.stanford.edu/main_notes.pdf
Used in 1 page
CS231n foundational notes ↗
https://cs231n.github.io/
Used in 1 page
Spring 2026 schedule ↗
https://cs231n.stanford.edu/schedule.html
Used in 2 pages
CS336 Spring 2026 ↗
https://cs336.stanford.edu/
Used in 4 pages
CS50P ↗
https://cs50.harvard.edu/python/
Used in 3 pages
Dive into Deep Learning ↗
https://d2l.ai/
Used in 1 page
Dagster — Assets ↗
https://dagster.io/docs/guides/build/assets
Used in 1 page
DataLemur — SQL & data science interview practice ↗
https://datalemur.com/
Used in 1 page
DataTalks.Club Slack ↗
https://datatalks.club/slack.html
Used in 1 page
Google Machine Learning Crash Course (refreshed edition) ↗
https://developers.google.com/machine-learning/crash-course/
Used in 1 page
Batch API ↗
https://developers.openai.com/api/docs/guides/batch
Used in 2 pages
prompt-caching guide ↗
https://developers.openai.com/api/docs/guides/prompt-caching
Used in 2 pages
Commission's current AI Act guidance ↗
https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
Used in 2 pages
Hugging Face forums ↗
https://discuss.huggingface.co/
Used in 1 page
DVC — Pipeline files ↗
https://doc.dvc.org/user-guide/project-structure/dvcyaml-files
Used in 1 page
uv ↗
https://docs.astral.sh/uv/
Used in 3 pages
locking and syncing ↗
https://docs.astral.sh/uv/concepts/projects/sync/
Used in 3 pages
installation instructions for your operating system ↗
https://docs.astral.sh/uv/getting-started/installation/
Used in 2 pages
uv: Working on projects and locking dependencies ↗
https://docs.astral.sh/uv/guides/projects/
Used in 1 page
AWS design guide ↗
https://docs.aws.amazon.com/prescriptive-guidance/latest/cloud-design-patterns/transactional-outbox.html
Used in 2 pages
Docker documentation — Get started, and the Python guide ↗
https://docs.docker.com/get-started/
Used in 2 pages
Evidently documentation ↗
https://docs.evidentlyai.com/introduction
Used in 1 page
Feast's point-in-time joins ↗
https://docs.feast.dev/getting-started/concepts/point-in-time-joins
Used in 5 pages
GitHub Actions documentation ↗
https://docs.github.com/en/actions
Used in 1 page
GitHub Actions — Building and testing Python ↗
https://docs.github.com/en/actions/tutorials/build-and-test-code/python
Used in 1 page
FAQ ↗
https://docs.ollama.com/faq
Used in 1 page
Polars User Guide ↗
https://docs.pola.rs/user-guide/getting-started/
Used in 1 page
pytest's getting-started guide ↗
https://docs.pytest.org/en/stable/getting-started.html
Used in 2 pages
torchao documentation ↗
https://docs.pytorch.org/ao/stable/
Used in 1 page
PyTorch's BatchNorm documentation ↗
https://docs.pytorch.org/docs/2.14/generated/torch.nn.BatchNorm2d.html
Used in 2 pages
loss documentation ↗
https://docs.pytorch.org/docs/2.14/generated/torch.nn.CrossEntropyLoss.html
Used in 2 pages
PyTorch reproducibility notes ↗
https://docs.pytorch.org/docs/2.14/notes/randomness.html
Used in 4 pages
serialisation notes ↗
https://docs.pytorch.org/docs/2.14/notes/serialization.html
Used in 1 page
PyTorch official tutorials — "Learn the Basics" ↗
https://docs.pytorch.org/tutorials/beginner/basics/intro.html
Used in 2 pages
PyTorch's optimisation tutorial ↗
https://docs.pytorch.org/tutorials/beginner/basics/optimization_tutorial.html
Used in 1 page
Saving and loading models ↗
https://docs.pytorch.org/tutorials/beginner/saving_loading_models.html
Used in 1 page
PyTorch — Fully Sharded Data Parallel, FSDP2 ↗
https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html
Used in 1 page
official compile tutorial ↗
https://docs.pytorch.org/tutorials/intermediate/torch_compile_tutorial.html
Used in 2 pages
PyTorch AMP recipe ↗
https://docs.pytorch.org/tutorials/recipes/recipes/amp_recipe.html
Used in 2 pages
Ray Serve ↗
https://docs.ray.io/en/latest/serve/index.html
Used in 1 page
SGLang documentation ↗
https://docs.sglang.io/
Used in 1 page
vLLM's serving benchmark ↗
https://docs.vllm.ai/en/stable/cli/bench/serve/
Used in 2 pages
current vLLM speculative-decoding guide ↗
https://docs.vllm.ai/en/stable/features/speculative_decoding/
Used in 2 pages
Roediger and Karpicke's 2006 experiments ↗
https://doi.org/10.1111/j.1467-9280.2006.01693.x
Used in 1 page
CUPED — Improving the Sensitivity of Online Controlled Experiments ↗
https://doi.org/10.1145/2433396.2433413
Used in 1 page
Dunlosky and colleagues' 2013 review ↗
https://doi.org/10.1177/1529100612453266
Used in 1 page
DuckDB documentation ↗
https://duckdb.org/docs/current/
Used in 1 page
his own hiring rubric ↗
https://eugeneyan.com/writing/how-to-interview/
Used in 4 pages
Patterns for Building LLM-based Systems & Products ↗
https://eugeneyan.com/writing/llm-patterns/
Used in 1 page
Trustworthy Online Controlled Experiments — companion site ↗
https://experimentguide.com/
Used in 1 page
The Matrix Calculus You Need For Deep Learning ↗
https://explained.ai/matrix-calculus/
Used in 1 page
FastAPI official documentation and tutorial ↗
https://fastapi.tiangolo.com/
Used in 1 page
lifespan guide ↗
https://fastapi.tiangolo.com/advanced/events/
Used in 2 pages
request-body ↗
https://fastapi.tiangolo.com/tutorial/body/
Used in 2 pages
testing ↗
https://fastapi.tiangolo.com/tutorial/testing/
Used in 3 pages
fast.ai forums ↗
https://forums.fast.ai/
Used in 1 page
OWASP prompt-injection reference ↗
https://genai.owasp.org/llmrisk/llm01-prompt-injection/
Used in 2 pages
OWASP LLM Top 10 2026 ↗
https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026/
Used in 3 pages
normcore-llm-reads ↗
https://gist.github.com/veekaybee/be375ab33085102f9027853128dc5f0e
Used in 2 pages
Pro Git, 2nd edition ↗
https://git-scm.com/book/en/v2
Used in 1 page
Approaching (Almost) Any Machine Learning Problem ↗
https://github.com/abhishekkrthakur/approachingalmost
Used in 2 pages
Hands-On Machine Learning with Scikit-Learn and PyTorch (2025) + official notebooks ↗
https://github.com/ageron/handson-mlp
Used in 1 page
official repository ↗
https://github.com/amazon-science/chronos-forecasting
Used in 1 page
Anthropic's Interactive Prompt Engineering Tutorial ↗
https://github.com/anthropics/prompt-eng-interactive-tutorial
Used in 1 page
CatBoost loss documentation ↗
https://github.com/catboost/catboost/blob/master/catboost/docs/en/concepts/loss-functions-regression.md
Used in 1 page
ML-YouTube-Courses ↗
https://github.com/dair-ai/ML-YouTube-Courses
Used in 1 page
LLM Zoomcamp ↗
https://github.com/DataTalksClub/llm-zoomcamp
Used in 1 page
MLOps Zoomcamp ↗
https://github.com/DataTalksClub/mlops-zoomcamp
Used in 1 page
fastbook ↗
https://github.com/fastai/fastbook
Used in 1 page
Computational Linear Algebra for Coders ↗
https://github.com/fastai/numerical-linear-algebra
Used in 1 page
llama.cpp ↗
https://github.com/ggml-org/llama.cpp
Used in 1 page
TimesFM 3's downloadable weights have noncommercial/nonproduction terms ↗
https://github.com/google-research/timesfm
Used in 2 pages
Deep Learning Tuning Playbook ↗
https://github.com/google-research/tuning_playbook
Used in 2 pages
notebooks ↗
https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
Used in 1 page
The Annotated Transformer — source and notebook ↗
https://github.com/harvardnlp/annotated-transformer
Used in 1 page
Transformers contributing guide ↗
https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md
Used in 2 pages
12-Factor Agents ↗
https://github.com/humanlayer/12-factor-agents
Used in 3 pages
nanochat ↗
https://github.com/karpathy/nanochat
Used in 3 pages
OpenTelemetry GenAI semantic conventions ↗
https://github.com/open-telemetry/semantic-conventions-genai
Used in 1 page
OpenAI Cookbook ↗
https://github.com/openai/openai-cookbook
Used in 1 page
Open Source Society University — Computer Science ↗
https://github.com/ossu/computer-science
Used in 1 page
TabPFN's code and newer weights have different terms ↗
https://github.com/PriorLabs/TabPFN
Used in 2 pages
PyTorch contributing guide ↗
https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md
Used in 1 page
companion repository ↗
https://github.com/rasbt/LLMs-from-scratch
Used in 1 page
free code ↗
https://github.com/rasbt/reasoning-from-scratch
Used in 1 page
correction issue ↗
https://github.com/santoshshinde2012/ai-ml-ground-truth/issues/new?template=correction.yml
Used in 1 page
released sampling routine ↗
https://github.com/seunghan96/DistPFN/blob/main/src/utils.py
Used in 1 page
TabICLv2 ↗
https://github.com/soda-inria/tabicl
Used in 2 pages
GroupLens MovieLens ↗
https://grouplens.org/datasets/movielens/
Used in 2 pages
The 2026 AI Index Report — Economy chapter ↗
https://hai.stanford.edu/ai-index/2026-ai-index-report/economy
Used in 1 page
Evals FAQ ↗
https://hamel.dev/blog/posts/evals-faq/
Used in 2 pages
Your AI Product Needs Evals ↗
https://hamel.dev/blog/posts/evals/
Used in 2 pages
AI and Job Postings: From Destruction to Creation? ↗
https://hiringlab.indeed.com/2026/07/08/ai-and-job-postings-from-destruction-to-creation/
Used in 2 pages
The Labor Market Is Tilting Toward Seniority ↗
https://hiringlab.indeed.com/2026/07/23/the-labor-market-is-tilting-toward-seniority/
Used in 1 page
Indeed's September 2026 US analysis ↗
https://hiringlab.indeed.com/2026/09/17/ai-exposure-isnt-squeezing-advertised-pay-in-the-us-its-boosting-it/
Used in 2 pages
Apache-2.0 checkpoint card ↗
https://huggingface.co/amazon/chronos-2
Used in 1 page
Hugging Face Spaces ↗
https://huggingface.co/docs/hub/spaces-overview
Used in 1 page
PEFT documentation ↗
https://huggingface.co/docs/peft/index
Used in 2 pages
safetensors ↗
https://huggingface.co/docs/safetensors/index
Used in 1 page
Transformers accumulation guide ↗
https://huggingface.co/docs/transformers/grad_accumulation
Used in 2 pages
TRL GRPO documentation ↗
https://huggingface.co/docs/trl/grpo_trainer
Used in 2 pages
TRL documentation ↗
https://huggingface.co/docs/trl/index
Used in 2 pages
checkpoint model card ↗
https://huggingface.co/jingang/TabICL
Used in 1 page
Hugging Face Agents Course ↗
https://huggingface.co/learn/agents-course
Used in 1 page
Hugging Face Context Course ↗
https://huggingface.co/learn/context-course
Used in 1 page
Hugging Face LLM Course ↗
https://huggingface.co/learn/llm-course/chapter1/1
Used in 1 page
Hugging Face MCP Course ↗
https://huggingface.co/learn/mcp-course/en/unit0/introduction
Used in 1 page
a smol course — post-training ↗
https://huggingface.co/learn/smol-course/en/unit0/1
Used in 1 page
AI Engineering: Building Applications with Foundation Models ↗
https://huyenchip.com/books/
Used in 1 page
imbalanced-learn's leakage examples ↗
https://imbalanced-learn.org/stable/common_pitfalls.html
Used in 3 pages
scikit-learn MOOC — Machine learning in Python with scikit-learn (Inria) ↗
https://inria.github.io/scikit-learn-mooc/
Used in 1 page
MIT 6.S191 — 2026 edition ↗
https://introtodeeplearning.com/
Used in 1 page
Python Data Science Handbook (free online edition) ↗
https://jakevdp.github.io/PythonDataScienceHandbook/
Used in 1 page
The Illustrated Transformer ↗
https://jalammar.github.io/illustrated-transformer/
Used in 1 page
Deliberate Practice and Performance: A Meta-Analysis ↗
https://journals.sagepub.com/doi/10.1177/0956797614535810
Used in 1 page
2018 correction ↗
https://journals.sagepub.com/doi/10.1177/0956797618769891
Used in 1 page
Ascarza — Retention Futility: Targeting High-Risk Customers Might Be Ineffective ↗
https://journals.sagepub.com/doi/10.1509/jmr.16.0163
Used in 2 pages
Neural Networks: Zero to Hero ↗
https://karpathy.ai/zero-to-hero.html
Used in 2 pages
recipe for training neural networks ↗
https://karpathy.github.io/2019/04/25/recipe/
Used in 2 pages
microgpt ↗
https://karpathy.github.io/2026/02/12/microgpt/
Used in 2 pages
Keras custom-training-step guide ↗
https://keras.io/guides/custom_train_step_in_torch/
Used in 1 page
Modern Polars ↗
https://kevinheavey.github.io/modern-polars/
Used in 1 page
Langfuse documentation ↗
https://langfuse.com/docs
Used in 1 page
GitHub Learn / GitHub Skills ↗
https://learn.github.com/skills
Used in 1 page
Microsoft's incrementality documentation ↗
https://learn.microsoft.com/en-us/xandr/data-science-toolkit/incrementality
Used in 1 page
Learn Git Branching ↗
https://learngitbranching.js.org/
Used in 1 page
Aalen–Johansen ↗
https://lifelines.readthedocs.io/en/latest/fitters/univariate/AalenJohansenFitter.html
Used in 2 pages
out-of-sample censor-aware horizon calibration ↗
https://lifelines.readthedocs.io/en/latest/lifelines.calibration.html
Used in 2 pages
lifelines survival regression ↗
https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html
Used in 1 page
lifelines time-varying survival guide ↗
https://lifelines.readthedocs.io/en/latest/Time%20varying%20survival%20regression.html
Used in 1 page
LightGBM parameters ↗
https://lightgbm.readthedocs.io/en/latest/Parameters.html
Used in 1 page
LightGBM documentation (Microsoft) and CatBoost documentation (Yandex) ↗
https://lightgbm.readthedocs.io/en/stable/
Used in 1 page
LightGBM advanced topics ↗
https://lightgbm.readthedocs.io/en/stable/Advanced-Topics.html
Used in 2 pages
LightGBM objective parameters ↗
https://lightgbm.readthedocs.io/en/stable/Parameters.html#is_unbalance
Used in 1 page
Lightning pricing ↗
https://lightning.ai/pricing
Used in 1 page
Lil'Log — research archive ↗
https://lilianweng.github.io/archives/
Used in 1 page
LLM Powered Autonomous Agents ↗
https://lilianweng.github.io/posts/2023-06-23-agent/
Used in 1 page
Harness Engineering for Self-Improvement (July 2026) ↗
https://lilianweng.github.io/posts/2026-07-04-harness/
Used in 1 page
Ahead of AI — newsletter/archive ↗
https://magazine.sebastianraschka.com/archive
Used in 1 page
marimo — reactive Python notebooks ↗
https://marimo.io/
Used in 1 page
AI Evals for Engineers & Product Managers ↗
https://maven.com/parlance-labs/evals
Used in 1 page
The Missing Semester of Your CS Education (IAP 2026 edition) ↗
https://missing.csail.mit.edu/
Used in 1 page
Causal Inference: The Remix ↗
https://mixtape.scunning.com/
Used in 1 page
MLflow — LLM and agent evaluation ↗
https://mlflow.org/docs/latest/genai/eval-monitor/
Used in 1 page
MLflow's regression-testing workflow ↗
https://mlflow.org/docs/latest/genai/eval-monitor/regression-testing/
Used in 2 pages
MLflow's registry workflow ↗
https://mlflow.org/docs/latest/ml/model-registry/workflow/
Used in 4 pages
MLflow — Experiment tracking ↗
https://mlflow.org/docs/latest/ml/tracking/
Used in 1 page
MLU-Explain — visual, interactive explanations of core ML concepts (Amazon Machine Learning University) ↗
https://mlu-explain.github.io/
Used in 1 page
Mathematics for Machine Learning (the mml-book) + free PDF ↗
https://mml-book.github.io/
Used in 1 page
direct PDF ↗
https://mml-book.github.io/book/mml-book.pdf
Used in 1 page
Modal pricing ↗
https://modal.com/pricing
Used in 2 pages
official MCP security practices ↗
https://modelcontextprotocol.io/docs/2026-07-28/tutorials/security/security_best_practices
Used in 2 pages
Model Context Protocol — official docs ↗
https://modelcontextprotocol.io/docs/getting-started/intro
Used in 1 page
2026-07-28 changelog ↗
https://modelcontextprotocol.io/specification/2026-07-28/changelog
Used in 2 pages
1.24 ↗
https://numpy.org/doc/2.3/release/1.24.0-notes.html
Used in 2 pages
2.0 reintroduced `np.bool` ↗
https://numpy.org/doc/stable/release/2.0.0-notes.html
Used in 2 pages
NumPy: the absolute basics for beginners ↗
https://numpy.org/doc/stable/user/absolute_beginners.html
Used in 2 pages
NIST AI RMF — Generative AI Profile ↗
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
Used in 1 page
MIT 18.065 — Matrix Methods in Data Analysis, Signal Processing, and Machine Learning ↗
https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/
Used in 1 page
MIT 18.06SC Linear Algebra (OCW Scholar edition) ↗
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Ollama ↗
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July update ↗
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OpenAI’s February 2026 audit ↗
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Why do tree-based models still outperform deep learning on typical tabular data? (NeurIPS 2022 Datasets & Benchmarks) ↗
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Forecasting: Principles and Practice ↗
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pandas migration guide ↗
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Claude prompt engineering docs ↗
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thinking-cost guide ↗
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Introduction to Probability for Data Science ↗
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Used in 1 page
Probabilistic Machine Learning: An Introduction ↗
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conference publication ↗
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Lancewicki et al. (ICML, 2021) ↗
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ICML 2026 DistPFN paper ↗
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Towards a Science of AI Agent Reliability (ICML 2026) ↗
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Used in 5 pages
2026 programming-classroom workshop paper ↗
https://proceedings.mlr.press/v339/heickal26a.html
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Sequential and adaptive policy evaluation ↗
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FlashAttention-4 ↗
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Gibbs and Candès ↗
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QLoRA: Efficient Finetuning of Quantized LLMs ↗
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Direct Preference Optimization ↗
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NeurIPS 2025 ↗
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Fisher, Gallino and Li — Competition-Based Dynamic Pricing in Online Retailing ↗
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Jiang and Li ↗
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PyTorch 2.14 release ↗
https://pytorch.org/blog/pytorch-2-14-release-blog/
Used in 2 pages
PyTorch installation guide ↗
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Official compute limits and pricing ↗
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Used in 2 pages
TimesFM 3 ↗
https://research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/
Used in 2 pages
Google — Deep Neural Networks for YouTube Recommendations ↗
https://research.google/pubs/deep-neural-networks-for-youtube-recommendations/
Used in 2 pages
AI and Data Scientist map ↗
https://roadmap.sh/ai-data-scientist
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AI Engineer Roadmap ↗
https://roadmap.sh/ai-engineer
Used in 1 page
scikit-learn lagged-features example ↗
https://scikit-learn.org/stable/auto_examples/applications/plot_time_series_lagged_features.html
Used in 2 pages
causal interpretation example ↗
https://scikit-learn.org/stable/auto_examples/inspection/plot_causal_interpretation.html
Used in 2 pages
scikit-learn — Poisson regression and non-normal loss ↗
https://scikit-learn.org/stable/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html
Used in 2 pages
scikit-learn supports Polars transformer output ↗
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Used in 2 pages
nested-CV example ↗
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scikit-learn: Common Pitfalls and Recommended Practices ↗
https://scikit-learn.org/stable/common_pitfalls.html
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scikit-learn contributing guide ↗
https://scikit-learn.org/stable/developers/contributing.html
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persistence guide ↗
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Used in 2 pages
scikit-learn's calibration guide ↗
https://scikit-learn.org/stable/modules/calibration.html
Used in 5 pages
threshold tuning guide ↗
https://scikit-learn.org/stable/modules/classification_threshold.html
Used in 2 pages
`CalibratedClassifierCV` ↗
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Used in 2 pages
`DummyClassifier(strategy="prior")` ↗
https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html
Used in 1 page
Average precision documentation ↗
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html
Used in 1 page
scikit-learn's TargetEncoder ↗
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html
Used in 2 pages
scikit-learn User Guide — Model Selection and Evaluation ↗
https://scikit-learn.org/stable/user_guide.html
Used in 1 page
time-dependent AUC assumptions ↗
https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.metrics.cumulative_dynamic_auc.html
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IBS documentation ↗
https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.metrics.integrated_brier_score.html
Used in 1 page
scikit-survival introduction ↗
https://scikit-survival.readthedocs.io/en/stable/user_guide/00-introduction.html
Used in 4 pages
survival boosting ↗
https://scikit-survival.readthedocs.io/en/stable/user_guide/boosting.html
Used in 2 pages
Competing risks and survival calibration ↗
https://scikit-survival.readthedocs.io/en/stable/user_guide/competing-risks.html
Used in 2 pages
scikit-survival — Evaluating survival models ↗
https://scikit-survival.readthedocs.io/en/stable/user_guide/evaluating-survival-models.html
Used in 2 pages
scikit-survival — Random survival forests ↗
https://scikit-survival.readthedocs.io/en/stable/user_guide/random-survival-forest.html
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Seeing Theory — A Visual Introduction to Probability and Statistics ↗
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Simon Willison's blog ↗
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SQLBolt — interactive SQL lessons ↗
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Site Reliability Engineering ↗
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October 6, 2026 ↗
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2026 Stack Overflow survey, released on 6 October ↗
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missing-data chapter ↗
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Effective Pandas 2: Opinionated Patterns for Data Manipulation ↗
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2025 AI section ↗
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2026 Stack Overflow Developer Survey ↗
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Tabular Foundation Models: A short and opinionated guide ↗
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The Effect ↗
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Used in 3 pages
Prince's _Understanding Deep Learning_ ↗
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Unsloth — fine-tuning docs and notebooks ↗
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essay on building AI applications ↗
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Vowpal Wabbit's contextual-bandit tutorial ↗
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Vowpal Wabbit — Contextual bandits ↗
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The MOOC Pivot ↗
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McKinney's _Python for Data Analysis_ ↗
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Neural Networks (Deep Learning series, chapters 1–7+) ↗
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Machine Learning Interview Prep Guide ↗
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Anthropic Engineering blog ↗
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Building Effective Agents ↗
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Used in 2 pages
Demystifying evals for AI agents (January 2026) ↗
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Effective context engineering for AI agents ↗
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March 2026 BrowseComp investigation ↗
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August 2026 incident account ↗
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CIRR verified bootcamp outcomes ↗
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Machine Learning Specialization (3 courses) ↗
https://www.coursera.org/specializations/machine-learning-introduction
Used in 1 page
Mathematics for Machine Learning and Data Science Specialization ↗
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Used in 1 page
Mathematics for Machine Learning Specialization (Imperial College London) ↗
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Appendix: Mathematics for Deep Learning (Dive into Deep Learning) ↗
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DeepLearning.AI Short Courses ↗
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Deep Learning ↗
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Statistical Learning with Python (StanfordOnline, edX) — free companion course to ISLP ↗
https://www.edx.org/learn/python/stanford-university-statistical-learning-with-python
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Trustworthy Online Controlled Experiments (companion site) ↗
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Kaggle Playground Series ↗
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Kaggle notebook documentation ↗
https://www.kaggle.com/docs/notebooks
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Kaggle Learn micro-courses (Python, Pandas, Data Cleaning, Data Visualization, Intro to ML) ↗
https://www.kaggle.com/learn
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Khan Academy — Linear Algebra, Multivariable Calculus, and Statistics & Probability ↗
https://www.khanacademy.org/math/statistics-probability
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The Rise of the AI Engineer ↗
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AI Engineer Compensation Trends (Q3 2025) ↗
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ML / AI Software Engineer salary tracker ↗
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Hands-On Large Language Models ↗
https://www.llm-book.com/
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Make It Stick: The Science of Successful Learning ↗
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Build a Large Language Model (From Scratch) ↗
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Build a Reasoning Model (From Scratch) ↗
https://www.manning.com/books/build-a-reasoning-model-from-scratch
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Microsoft — Diagnosing sample-ratio mismatch ↗
https://www.microsoft.com/en-us/research/articles/diagnosing-sample-ratio-mismatch-in-a-b-testing/
Used in 3 pages
Microsoft pre-experiment practices ↗
https://www.microsoft.com/en-us/research/articles/patterns-of-trustworthy-experimentation-pre-experiment-stage/
Used in 2 pages
Microsoft's pre-experiment guidance ↗
https://www.microsoft.com/en-us/research/group/experimentation-platform-exp/articles/patterns-of-trustworthy-experimentation-pre-experiment-stage/
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Diagnosing Sample Ratio Mismatch in Online Controlled Experiments ↗
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DeepSeek-R1 ↗
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Lee's sample-selection analysis ↗
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Conlon and Mortimer — Demand Estimation Under Incomplete Product Availability ↗
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Teach Yourself Programming in Ten Years ↗
https://www.norvig.com/21-days.html
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publisher record ↗
https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/
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Metalearners for estimating heterogeneous treatment effects ↗
https://www.pnas.org/doi/10.1073/pnas.1804597116
Used in 3 pages
PwC 2026 Global AI Jobs Barometer ↗
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official release ↗
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Used in 1 page
Python for Everybody (PY4E) ↗
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Used in 1 page
Python 3 book ↗
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Used in 1 page
instrumental variables ↗
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Used in 2 pages
EconML — Double machine learning ↗
https://www.pywhy.org/EconML/spec/estimation/dml.html
Used in 2 pages
EconML — Doubly robust learning ↗
https://www.pywhy.org/EconML/spec/estimation/dr.html
Used in 4 pages
retrieve-then-rerank example ↗
https://www.sbert.net/examples/sentence_transformer/applications/retrieve_rerank/README.html
Used in 2 pages
State of AI Report 2026 ↗
https://www.stateof.ai/
Used in 2 pages
_Introduction to Statistical Learning_ ↗
https://www.statlearning.com/
Used in 3 pages
family documentation ↗
https://www.statsmodels.org/stable/generated/statsmodels.genmod.families.family.NegativeBinomial.html
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statsmodels documentation ↗
https://www.statsmodels.org/stable/generated/statsmodels.stats.contingency_tables.mcnemar.html
Used in 1 page
statsmodels — Generalized linear models ↗
https://www.statsmodels.org/stable/glm.html
Used in 2 pages
retrieval task API ↗
https://www.tensorflow.org/recommenders/api_docs/python/tfrs/tasks/Retrieval
Used in 2 pages
TensorFlow Recommenders — Basic retrieval ↗
https://www.tensorflow.org/recommenders/examples/basic_retrieval
Used in 2 pages
side-feature tutorial ↗
https://www.tensorflow.org/recommenders/examples/featurization
Used in 2 pages
The Hundred-Page Machine Learning Book ↗
https://www.themlbook.com/
Used in 1 page
Mode / ThoughtSpot SQL Tutorial (Basic, Intermediate, Advanced) ↗
https://www.thoughtspot.com/sql-tutorial
Used in 2 pages
StatQuest with Josh Starmer (Statistics Fundamentals, Machine Learning, Neural Networks playlists) ↗
https://www.youtube.com/@statquest/playlists
Used in 1 page
_Essence of Linear Algebra_ ↗
https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
Used in 2 pages
Deep Dive into LLMs like ChatGPT ↗
https://www.youtube.com/watch?v=7xTGNNLPyMI
Used in 2 pages
build-a-GPT lecture ↗
https://www.youtube.com/watch?v=kCc8FmEb1nY
Used in 2 pages
XGBoost official documentation ↗
https://xgboost.readthedocs.io/en/stable/
Used in 1 page
Let's build the GPT Tokenizer ↗
https://youtu.be/zduSFxRajkE
Used in 1 page

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