References & Resources
A curated collection of books, research papers, online courses, and other resources to deepen your understanding of machine learning and deep learning.
Books
by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
A comprehensive introduction to deep learning, covering both theoretical foundations and practical applications.
View Book →by Christopher Bishop
A classic textbook covering the mathematical foundations of machine learning with a Bayesian perspective.
by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
A comprehensive overview of statistical learning methods for data mining, inference, and prediction.
View Book →by Aurélien Géron
A practical guide to implementing machine learning algorithms with popular Python libraries.
Research Papers
by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton
The seminal paper introducing AlexNet, which demonstrated the power of deep convolutional neural networks for image classification.
by Ashish Vaswani et al.
Introduced the Transformer architecture, which has revolutionized natural language processing and other sequence modeling tasks.
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
Introduced residual networks (ResNets), which enabled the training of much deeper neural networks.
Online Courses
Stanford University • Instructor: Andrew Ng
A comprehensive introduction to machine learning covering supervised and unsupervised learning, deep learning, and reinforcement learning.
View Course →Coursera • Instructor: Andrew Ng
A series of courses covering the foundations of deep learning, CNNs, sequence models, and practical aspects of deep learning projects.
fast.ai • Instructor: Jeremy Howard and Rachel Thomas
A top-down, practical approach to deep learning that gets students building models from day one.
View Course →