Recommended Reading

These AI/ML resources enhance the AI/ML in Rust tutorial, supporting Core ML, Deep Learning, Advanced Topics, and Projects like Sentiment Analysis and Customer Churn Prediction. They align with Rust’s ML ecosystem (linfa, tch-rs, polars) for efficient, safe AI/ML development.

• Hands-On Machine Learning by Aurélien Géron: oreilly.com
• Deep Learning by Ian Goodfellow, Yoshua Bengio, & Aaron Courville: deeplearningbook.org
• An Introduction to Statistical Learning by Gareth James et al.: statlearning.com
• Bayesian Data Analysis by Andrew Gelman et al.: columbia.edu
• Recommender Systems by Fabio Ricci et al.: springer.com
• Pattern Recognition and Machine Learning by Christopher Bishop: springer.com
• Machine Learning Yearning by Andrew Ng: ml-yearning.com
• Neural Networks and Deep Learning by Michael Nielsen: neuralnetworksanddeeplearning.com
• DeepLearning.AI ML Specialization by Andrew Ng: coursera.org
• Fast.ai Practical Deep Learning: course.fast.ai
• Stanford CS229: Machine Learning by Andrew Ng: cs229.stanford.edu
• Google ML Crash Course: developers.google.com
• Towards Data Science: AI/ML articles. towardsdatascience.com
• Distill: Deep learning research. distill.pub
• Papers with Code: ML papers and code. paperswithcode.com

Next Steps

Proceed to Communities or revisit Rust ML Libraries.