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