References & Further Reading

References & Further Reading#

“Because great data scientists stand on the shoulders of giants… and occasionally Google.”

⚠️ A Note Before You Dive In#

This reference section is a collaborative work in progress. That means you (yes, you) are warmly invited to contribute your favorite research papers, blogs, datasets, or meme-worthy ML resources.

💬 Think of it like open-source academia — minus the paywalls and with slightly better jokes.

If you’d like to contribute:

  • Fork the repo 🪓

  • Add your sources in Markdown format

  • Make a pull request titled “I read something cool”

🧠 Core Machine Learning References#

  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.

  • Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly.

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

  • Murphy, K. P. (2023). Probabilistic Machine Learning: Advanced Topics. MIT Press.

🏢 ML for Business & Applied Analytics#

  • Provost, F., & Fawcett, T. (2013). Data Science for Business. O’Reilly.

  • Shmueli, G., Bruce, P. C., Gedeck, P., Patel, N. R. (2020). Data Mining for Business Analytics. Wiley.

  • McKinsey Global Institute Reports on AI Impact (2018–2024) — surprisingly good PowerPoints with surprisingly real data.

  • Kaggle Competitions – Because nothing teaches business metrics like losing gracefully.

🧮 Optimization & Math Foundations#

  • Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.

  • Nocedal, J., & Wright, S. (2006). Numerical Optimization. Springer.

  • MIT OCW 6.036 — Introduction to Machine Learning. Free, legendary, and full of “aha” moments.

🤖 Deep Learning & PyTorch References#

  • PyTorch Documentation – https://pytorch.org/docs/stable/

  • Karpathy, A. – “A Recipe for Training Neural Networks”

  • Stanford CS231n – Convolutional Neural Networks for Visual Recognition

  • Hugging Face – Transformers and Datasets Documentation

  • FastAI – Deep Learning for Coders (where you realize Jupyter notebooks can be spiritual experiences)

🧩 Generative & LLM Resources#

  • Vaswani, A. et al. (2017). “Attention Is All You Need.” NeurIPS.

  • OpenAI Technical Reports – GPT series papers

  • Anthropic’s Claude Research Papers

  • Hugging Face Hub — like the App Store, but for massive models.

  • Lilian Weng’s Blog – Deep dives with sanity and humor.

⚙️ MLOps, Monitoring, and Production#

  • Introducing MLOps – Google Cloud Whitepaper (2021)

  • Practical MLOps – Noah Gift (O’Reilly)

  • MLflow Docs – https://mlflow.org/docs/

  • Weights & Biases Guides – because “tracking experiments” should not involve Excel.

🧾 Time Series, Forecasting & Business Apps#

  • Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice. (Free Online Textbook)

  • Taylor, S. J., & Letham, B. (2018). Prophet: Forecasting at Scale.

  • Makridakis, S., et al. The M Competitions – where forecasting models go to be humbled.

🧪 Causal Inference & Experimentation#

  • Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If.

  • Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences.

  • “The Book of Why” – Judea Pearl, Dana Mackenzie.

  • Airbnb Tech Blog – Real-world A/B testing gone beautifully right (and sometimes wrong).

🧑‍🤝‍🧑 Collaborative Credits#

This section will grow as readers, students, and fellow data wranglers contribute their favorite sources.

🪶 To add your references:

  1. Edit references.md

  2. Follow the style used here

  3. Add a quick funny line if possible — learning is better when you smile.

# Your code here
Contents