Resources

Learning materials and references for machine learning and science

Learning Resources

Curated collection of books, papers, courses, and tools for machine learning and scientific computing.

📚 Essential Books

Machine Learning

Pattern Recognition and Machine Learning - Christopher Bishop
The Elements of Statistical Learning - Hastie, Tibshirani, Friedman
Hands-On Machine Learning - Aurélien Géron
Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville

Scientific Computing

Python for Data Analysis - Wes McKinney
Computational Physics - Mark Newman
Numerical Recipes - Press, Teukolsky, Vetterling, Flannery

🎓 Online Courses

Machine Learning

Scientific Computing

🛠️ Tools and Frameworks

Python Ecosystem

Core: NumPy, Pandas, Matplotlib, Seaborn
ML: Scikit-learn, PyTorch, TensorFlow, JAX
Stats: SciPy, Statsmodels, PyMC
Viz: Plotly, Altair, Bokeh

R Ecosystem

Tidyverse: dplyr, ggplot2, tidyr
ML: caret, randomForest, e1071
Stats: lme4, survival, forecast

Julia

Core: DataFrames.jl, Plots.jl
ML: MLJ.jl, Flux.jl
Scientific: DifferentialEquations.jl

📄 Key Papers

Foundational

Rosenblatt (1958) - The Perceptron
Rumelhart et al. (1986) - Backpropagation
Vapnik (1995) - Support Vector Networks

Modern Deep Learning

Attention Is All You Need (2017) - Transformers
BERT (2018) - Bidirectional Transformers
GPT series - Language Models

Scientific ML

Physics-Informed Neural Networks
Neural ODEs
Graph Neural Networks

🌐 Websites and Blogs

Technical Blogs

Distill.pub - Interactive ML explanations
Papers With Code - Latest research
Towards Data Science - Medium publication

Reference

📊 Datasets

General ML

Scientific

This page is continuously updated with new and relevant resources. Suggestions welcome!

--- title: "Resources" description: "Learning materials and references for machine learning and science" --- # Learning Resources Curated collection of books, papers, courses, and tools for machine learning and scientific computing. ## 📚 Essential Books ### Machine Learning - **Pattern Recognition and Machine Learning** - Christopher Bishop - **The Elements of Statistical Learning** - Hastie, Tibshirani, Friedman - **Hands-On Machine Learning** - Aurélien Géron - **Deep Learning** - Ian Goodfellow, Yoshua Bengio, Aaron Courville ### Scientific Computing - **Python for Data Analysis** - Wes McKinney - **Computational Physics** - Mark Newman - **Numerical Recipes** - Press, Teukolsky, Vetterling, Flannery ## 🎓 Online Courses ### Machine Learning - [CS229 - Stanford Machine Learning](https://cs229.stanford.edu/) - [Deep Learning Specialization - Coursera](https://www.coursera.org/specializations/deep-learning) - [Fast.ai Practical Deep Learning](https://course.fast.ai/) ### Scientific Computing - [Scientific Computing with Python - FreeCodeCamp](https://www.freecodecamp.org/learn/scientific-computing-with-python/) - [Computational Thinking - MIT](https://computationalthinking.mit.edu/) ## 🛠️ Tools and Frameworks ### Python Ecosystem - **Core**: NumPy, Pandas, Matplotlib, Seaborn - **ML**: Scikit-learn, PyTorch, TensorFlow, JAX - **Stats**: SciPy, Statsmodels, PyMC - **Viz**: Plotly, Altair, Bokeh ### R Ecosystem - **Tidyverse**: dplyr, ggplot2, tidyr - **ML**: caret, randomForest, e1071 - **Stats**: lme4, survival, forecast ### Julia - **Core**: DataFrames.jl, Plots.jl - **ML**: MLJ.jl, Flux.jl - **Scientific**: DifferentialEquations.jl ## 📄 Key Papers ### Foundational - Rosenblatt (1958) - The Perceptron - Rumelhart et al. (1986) - Backpropagation - Vapnik (1995) - Support Vector Networks ### Modern Deep Learning - Attention Is All You Need (2017) - Transformers - BERT (2018) - Bidirectional Transformers - GPT series - Language Models ### Scientific ML - Physics-Informed Neural Networks - Neural ODEs - Graph Neural Networks ## 🌐 Websites and Blogs ### Technical Blogs - [Distill.pub](https://distill.pub/) - Interactive ML explanations - [Papers With Code](https://paperswithcode.com/) - Latest research - [Towards Data Science](https://towardsdatascience.com/) - Medium publication ### Reference - [scikit-learn User Guide](https://scikit-learn.org/stable/user_guide.html) - [PyTorch Tutorials](https://pytorch.org/tutorials/) - [Hugging Face Documentation](https://huggingface.co/docs) ## 📊 Datasets ### General ML - [UCI ML Repository](https://archive.ics.uci.edu/ml/) - [Kaggle Datasets](https://www.kaggle.com/datasets) - [Google Dataset Search](https://datasetsearch.research.google.com/) ### Scientific - [NASA Open Data](https://data.nasa.gov/) - [NCBI Databases](https://www.ncbi.nlm.nih.gov/guide/databases/) - [Climate Data Store](https://cds.climate.copernicus.eu/) --- *This page is continuously updated with new and relevant resources. Suggestions welcome!*