With an aim of developing a training course in machine / deep learning for life-science and chemistry researchers, this is a compilation of relevant materials.
Other:
- de la Vega de León (r), Effect of missing data on multitask prediction methods
- Liu, Deep EHR: Chronic Disease Prediction Using Medical Notes
- Radovic, Machine learning at the energy and intensity frontiers of particle physics
- Ryan, Crystal Structure Prediction via Deep Learning
- Sanchez-Lengeling and Aspuru-Guzik, Inverse molecular design using machine learning: Generative models for matter engineering
- Way, Discovering pathway and cell-type signatures in transcriptomic compendia with machine learning
- Chen, XGBoost: A Scalable Tree Boosting System
- Vinyals, Matching Networks for One Shot Learning: learning with little data
- Simm, Macau: Scalable Bayesian Multi-relational Factorization with Side Information using MCMC
| Authors | Title / Link |
|---|---|
| Nielsen | Neural Networks and Deep Learning |
| James, Witten, Hastie and Tibshirani | An Introduction to Statistical Learning with Applications in R |
| Hastie, Tibshirani and Friedman | The Elements of Statistical Learning: Data Mining, Inference, and Prediction |
| Goodfellow, Bengio and Courville | Deep Learning |
| Provider | Title / Link |
|---|---|
| Parr and Howard | How to explain gradient boosting |
| Machine learning for kids | projects |
| Data flair | Machine learning tutorials and Artificial Intelligence tutorials |
| MIT | Introduction to Deep Learning |
| fast.ai | Deep learning and machine learning courses |
| Coursera | Deep Learning Specialization |
| University of Cambridge | An Introduction to Machine Learning: github and book |
| Data School | In-depth introduction to machine learning in 15 hours of expert videos |
| Shirin Glander | Introduction to Machine Learning with R |
Implementations:
| Language | Library | Description |
|---|---|---|
| Python | scikit-learn | machine learning |
| Python | PyTorch | deep learning, Facebook |
| Python | Keras | deep learning |
| Python | TensorFlow | deep learning, Google |
| Python | Theano | deep learning |
| Python | DragoNN | deep learning for genomics |
| Python | Macau | Bayesian factorization |
| R | h2o | machine and deep learning |
| R | mlr | machine learning |
| R | caret | machine learning |
| R | randomForest | Breiman and Cutler's Random Forests for Classification and Regression |
| Java | WEKA | machine learning |
Repositories:
| Title / Link | Description |
|---|---|
| Kipoi | machine and deep learning models in genomics |
Blogs:
- Primer for Learning Google Colab
- Scikit-Learn: A silver bullet for basic machine learning
- How to build your own Neural Network from scratch in Python
- Every single Machine Learning course on the internet, ranked by your reviews
Cheat sheets:
- 101 Machine Learning Algorithms for Data Science with Cheat Sheets
- Essential cheat sheets for deep learning and machine learning researchers
- RStudio Machine Learning Modelling
- RStudio Deep Learning with Keras
- RStudio caret package
- RStudio mlr package
- RStudio h2o environment
CRAN Task Views:
- Pimentel's collection of deep learning papers