Resources
Wish these also work for you!
Guides for Optimization
- Convex Optimization – by S. Boyd and L. Vandenberghe
- Convex Analysis and Minimization Algorithms – by J.-B. Hiriart-Urruty and C. Lemaréchal
- Numerical Optimization – by J. Nocedal and S. J. Wright
- Optimization Methods for Large-Scale Machine Learning – by L. Bottou, F. E. Curtis and J. Nocedal
How to dive into machine learning
- The Elements of Statistical Learning – by T. Hastie, R. Tibshirani and J. Friedman
- Deep Learning – by I. Goodfellow, Y. Bengio and A. Courville
- Dive Into Deep Learning – by A. Zhang, Z. C. Lipton, M. Li and A. J. Smola
- Hands-on Machine Learning with Scikit-Learn & TensorFlow – by A. Géron
- Speech and Language Processing – by D. Jurafsky and J. H. Martin
- Probabilistic Graphical Models – by E. P. Xing and course staff in Carnegie Mellon
Friendly Mathematics
- An Introduction to Stochastic Differential Equations – by L. C. Evans
- Functional Analysis – by P. D. Lax
- Lectures on Real Analysis – by M. Tyaglov
- Complex Analysis – by T. W. Gamelin
Get to know Cheminformatics
- Analyzing Learned Molecular Representations for Property Prediction
- Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
- Deep Graph Library (DGL)
- Geometric Deep Learning Extension Library for Pytorch
- Getting Started with the RDKit in Python
For potential graduates & researchers
- The Ph.D. Grind – by P. J. Guo
- The Ph.D. Grind (Chinese Version) by Q. Peng and Y. Luo