Access research papers, books, datasets, and academic resources supporting AI education and research.
RESEARCH PAPER • 2017
Authors: Vaswani et al.
Venue: NeurIPS 2017
Groundbreaking paper introducing the Transformer architecture, revolutionizing natural language processing and becoming the foundation for modern language models like BERT and GPT.
RESEARCH PAPER • 2012
Authors: Krizhevsky, Sutskever, Hinton
Venue: NeurIPS 2012
AlexNet paper demonstrating that deep convolutional neural networks significantly outperform previous methods on image classification, sparking the deep learning revolution.
TEXTBOOK • 4TH EDITION
Authors: Stuart Russell, Peter Norvig
Comprehensive introduction to AI covering problem-solving, knowledge representation, planning, machine learning, and ethics. Widely adopted textbook for undergraduate and graduate AI courses globally.
IMAGE DATASET • CLASSIFICATION
Standard benchmark datasets for image classification. CIFAR-10 contains 60,000 32x32 color images in 10 classes. CIFAR-100 has 100 classes. Widely used for evaluating computer vision algorithms.
RESEARCH PAPER • 2013
Authors: Mnih et al. (DeepMind)
Venue: NeurIPS Workshop 2013
Introduced Deep Q-Networks (DQN), combining deep learning with reinforcement learning to achieve human-level performance on Atari games, pioneering deep reinforcement learning.
TEXTBOOK • CONCISE GUIDE
Author: Andriy Burkov
Concise introduction covering fundamental ML concepts, algorithms, and practical considerations. Designed for busy professionals seeking a quick but comprehensive overview of machine learning.