Tutorials

Statistics Tutorials
R Tutorials
Sagemath Tutorials

ML channels

1. Machine Learning Made Simple Podcast
2. Machine Learning Made Simple Video Channel
3. DeepLearningAI
4. a

ML papers

Machine Learning Methods

1. Attention Is All You Need, Ashish Vaswani et al., arXiv:1706.03762
2. An Introduction to Variational Autoencoders, Diederik P. Kingma et al., arXiv:1906.02691
3. Mixtral of Experts, Albert Q. Jiang et al., arXiv:2401.04088

Fine-Tuning and Adaptation

1. LoRA: Low-Rank Adaptation of Large Language Models, Edward Hu et al., arXiv:2106.09685
2. QLoRA: Efficient Finetuning of Quantized LLMs, Tim Dettmers et al., arXiv:2305.14314

Evaluations

1. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding: Alex Wang et al., arXiv:1804.07461
2. HellaSwag: Can a Machine Really Finish Your Sentence?, Rowan Zellers et al., arXiv:1905.07830

Surveys

1. A Survey of Large Language Models, Wayne Xin Zhao et al., arXiv:2303.18223
2. Retrieval-Augmented Generation for Large Language Models: A Survey, Yunfan Gao et al., arXiv:2312.10997

Language Models

1. GPT-3: Language Models are Few-Shot Learners, Tom B. Brown et al., arXiv:2005.14165
2. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, Colin Raffel et al., arXiv:1910.10683
3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin et al., arXiv:1810.04805
4. RoBERTa: A Robustly Optimized BERT Approach for Natural Language Understanding, Yinhan Liu et al., arXiv:1907.11692
5. XLNet: Generalized Autoregressive Pretraining for Language Understanding, Zhilin Yang et al., arXiv:1906.08237
6. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, Zhenzhong Lan et al., arXiv:1909.11942
7. DistilBERT: A Distilled Version of BERT: Smaller, Faster, Cheaper and Lighter, Victor Sanh et al., arXiv:1910.01108
8. ERNIE: Enhanced Representation through kNowledge Integration, Yu Sun et al., arXiv:1904.09223
9. XLM-R: Unsupervised Cross-lingual Representation Learning at Scale, Alexis Conneau et al., arXiv:1911.02116
10. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators, Kevin Clark et al., arXiv:2003.10555
11. Reformer: The Efficient Transformer, Nikita Kitaev et al., arXiv:2001.04451
12. Longformer: The Long-Document Transformer, Iz Beltagy et al., arXiv:2004.05150

Important Websites in the ML World

1. For (State-of-the-art) SOTA models see Hugging Face
2. For connecting papers with their associated codes see paperswithcode