Resources below are for me as well as visitors of this page. These include data science, machine/deep learning related sources that I have visited before and that I found very instructive.

Books

Deep Learning (Ian Goodfellow, Yoshua Bengio, Aaron Courville)
A beginner-intermediate level source for understanding what deep learning is, and for having an intuitive grasp of the material.

Reinforcement Learning: An Introduction (Andrew Barto, Richard S. Sutton)
As it is clear from its name, this is a fantastic introduction to reinforcement learning.

Introduction to Linear Algebra (Gilbert Strang)
It is imperative to know fundamentals of Linear Algebra to do machine learning research, and this is a fantastic source.

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Chip Huyen)
This is a book for the people working on deployment part. However, it is still a good source for researchers to appreciate the difficulty of maintaining these models for real customers.

Mathematics for Machine Learning (Marc Peter Deisenroth et. al.)
Not sure if it is a good book for beginners, but a great one as a refresher.

Statistical Tests (DATATab)
Very good source for understanding statistical tests.

Online Lectures

Deep Generative Models (Volodymyr Kuleshov)
The best resource out there for understanding generative models (as of now). The instructor makes a fabulous job in connecting different developments in generative modeling which makes it very easy to understand and follow.

Linear Algebra (Gilbert Strang)
Another perfect source from Prof. Strang. These video lectures are old but the contents are still fresh for machine learning researchers.

Reinforcement Learning Course (David Silver)
This was the source that I learned reinforcement learning from. Content might be a bit outdated but wonderful for getting the fundamentals.

Stanford Machine Learning Course (Andrew Ng)
This course is perfect for conventional machine learning basics.

Stanford CS231n - 2017 Video Lecture Series
Great source for understanding the basics of deep learning.

Applied Machine Learning - Cornell (Volodymyr Kuleshov) I used this one as a refresher. Very explanatory for the basics of conventional ML algorithms.

Websites/Videos

Understanding LSTM Networks (Christopher Olah)

Papers

• LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436-444.
• Vaswani, Ashish, et al. “Attention is all you need.” Advances in neural information processing systems 30 (2017). Transformer Paper
• Mnih, Volodymyr, et al. “Playing atari with deep reinforcement learning.” arXiv preprint arXiv:1312.5602 (2013). DQN Paper
• Lillicrap, Timothy P., et al. “Continuous control with deep reinforcement learning.” arXiv preprint arXiv:1509.02971 (2015). DDPG Paper
• Haarnoja, Tuomas, et al. “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.” International conference on machine learning. PMLR, 2018. SAC Paper
• Fujimoto, Scott, Herke Hoof, and David Meger. “Addressing function approximation error in actor-critic methods.” International conference on machine learning. PMLR, 2018. TD3 Paper
• Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. “U-net: Convolutional networks for biomedical image segmentation.” Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer International Publishing, 2015. U-Net Paper
• Ploetz, Thomas. “Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them.” ACM Computing Surveys (CSUR) 54.6 (2021): 1-25.
• Oord, Aaron van den, Yazhe Li, and Oriol Vinyals. “Representation learning with contrastive predictive coding.” arXiv preprint arXiv:1807.03748 (2018). InfoNCE Loss
• Doersch, Carl. “Tutorial on variational autoencoders.” arXiv preprint arXiv:1606.05908 (2016). VAE
• Devlin, Jacob, et al. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018). BERT
• Liang, Paul Pu, Amir Zadeh, and Louis-Philippe Morency. “Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions.” ACM Computing Surveys (2023). Multi-modality
• Fairclough, Stephen H. “Fundamentals of physiological computing.” Interacting with computers 21.1-2 (2009): 133-145. Psychophysiological Computing
• Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. “Isolation forest.” 2008 eighth ieee international conference on data mining. IEEE, 2008. Isolation Forest
• Hochreiter, Sepp, and Jürgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735-1780. LSTM
• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems 25 (2012). AlexNet
• Schölkopf, Bernhard, et al. “Toward causal representation learning.” Proceedings of the IEEE 109.5 (2021): 612-634. Causal Learning
• Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. R-CNN
• Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision. 2015. Fast R-CNN
• Ren, Shaoqing, et al. “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems 28 (2015). Faster R-CNN
• Long, Jonathan, Evan Shelhamer, and Trevor Darrell. “Fully convolutional networks for semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. FCN
• Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. YOLO
• Burges, Christopher JC. “A tutorial on support vector machines for pattern recognition.” Data mining and knowledge discovery 2.2 (1998): 121-167. SVM
• Breiman, Leo. “Random forests.” Machine learning 45 (2001): 5-32. Random Forests
• Bengio, Yoshua, Aaron Courville, and Pascal Vincent. “Representation learning: A review and new perspectives.” IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828. Representation Learning
• He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. ResNet
• He, Kaiming, et al. “Mask r-cnn.” Proceedings of the IEEE international conference on computer vision. 2017. Mask R-CNN
• Carion, Nicolas, et al. “End-to-end object detection with transformers.” European conference on computer vision. Cham: Springer International Publishing, 2020.
• Mildenhall, Ben, et al. “Nerf: Representing scenes as neural radiance fields for view synthesis.” Communications of the ACM 65.1 (2021): 99-106. Nerf
• Touvron, Hugo, et al. “Training data-efficient image transformers & distillation through attention.” International conference on machine learning. PMLR, 2021. Data-efficient image transformers (DeiT)
• Dosovitskiy, Alexey, et al. “An image is worth 16x16 words: Transformers for image recognition at scale.” arXiv preprint arXiv:2010.11929 (2020). ViT
• Liu, Ze, et al. “Swin transformer: Hierarchical vision transformer using shifted windows.” Proceedings of the IEEE/CVF international conference on computer vision. 2021. SWIN
• Rombach, Robin, et al. “High-resolution image synthesis with latent diffusion models.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022. Latent Diffusion Models
• Bao, Fan, et al. “All are worth words: A vit backbone for diffusion models.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023. U-ViT
• Radford, Alec, et al. “Learning transferable visual models from natural language supervision.” International conference on machine learning. PMLR, 2021. CLIP
• Yu, Jiahui, et al. “Coca: Contrastive captioners are image-text foundation models.” arXiv preprint arXiv:2205.01917 (2022). CoCa
• Li, Boyi, et al. “Language-driven semantic segmentation.” arXiv preprint arXiv:2201.03546 (2022).
• Gu, Albert, and Tri Dao. “Mamba: Linear-time sequence modeling with selective state spaces.” arXiv preprint arXiv:2312.00752 (2023). MAMBA
• Qu, Haohao, et al. “A survey of mamba.” arXiv preprint arXiv:2408.01129 (2024).
• Shumailov, Ilia, et al. “AI models collapse when trained on recursively generated data.” Nature 631.8022 (2024): 755-759.