| 1. | Classification of sentiment reviews using n-gram machine learning approach | 171 | Review |
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| 2. | Combining Satellite Imagery and machine learning to predict poverty | 52 | |
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| 3. | [BERT] Pretranied Deep Bidirectional Transformers for Language Understanding (algorithm) | TDLS | 39 | |
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| 4. | [Transformer] Attention Is All You Need | AISC Foundational | 38 | |
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| 5. | Deep Neural Networks for YouTube Recommendation | AISC Foundational | 20 | |
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| 6. | [Variational Autoencoder] Auto-Encoding Variational Bayes | AISC Foundational | 18 | |
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| 7. | Connectionist Temporal Classification, Labelling Unsegmented Sequence Data with RNN | TDLS | 18 | |
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| 8. | All-optical machine learning using diffractive deep neural networks | TDLS | 18 | |
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| 9. | Can deep learning AI help with detecting COVID-19/coronavirus? | 17 | |
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| 10. | [StyleGAN] A Style-Based Generator Architecture for GANs, part 1 (algorithm review) | TDLS | 16 | Review |
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| 11. | [Original ResNet paper] Deep Residual Learning for Image Recognition | AISC | 16 | |
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| 12. | AlphaStar explained: Grandmaster level in StarCraft II with multi-agent RL | 14 | Let's Play | StarCraft II
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| 13. | Introduction to the Conditional GAN - A General Framework for Pixel2Pixel Translation | 14 | |
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| 14. | Convolutional Neural Networks for processing EEG signals | 12 | |
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| 15. | Junction Tree Variational Autoencoder for Molecular Graph Generation | TDLS | 11 | |
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| 16. | Recurrent Models of Visual Attention | TDLS | 11 | |
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| 17. | Principles of Riemannian Geometry in Neural Networks | TDLS | 11 | |
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| 18. | [ELMo] Deep Contextualized Word Representations | AISC | 11 | |
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| 19. | [StackGAN++] Realistic Image Synthesis with Stacked Generative Adversarial Networks | AISC | 9 | |
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| 20. | (Original Paper) Latent Dirichlet Allocation (algorithm) | AISC Foundational | 9 | |
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| 21. | Introduction to NVIDIA NeMo - A Toolkit for Conversational AI | AISC | 8 | |
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| 22. | [GAT] Graph Attention Networks | AISC Foundational | 8 | |
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| 23. | A Literature Review on Graph Neural Networks | 8 | Review |
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| 24. | Lagrangian Neural Networks | AISC | 7 | |
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| 25. | [DDQN] Deep Reinforcement Learning with Double Q-learning | TDLS Foundational | 7 | |
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| 26. | [Original attention] Neural Machine Translation by Jointly Learning to Align and Translate | AISC | 7 | |
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| 27. | Supercharging AI with high performance distributed computing | 7 | |
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| 28. | Mathematics of Deep Learning Overview | AISC Lunch & Learn | 6 | |
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| 29. | Transformer XL | AISC Trending Papers | 6 | |
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| 30. | TGN: Temporal Graph Networks for Deep Learning on Dynamic Graphs [Paper Explained by the Author] | 6 | |
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| 31. | Leaf Doctor: Plant Disease Detection Using Image Classification | Deep Learning Workshop Capstone | 5 | |
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| 32. | Revolutionary Deep Learning Method to Denoise EEG Brainwaves | 5 | |
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| 33. | Sparse Transformers and MuseNet | AISC | 5 | |
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| 34. | Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond | TDLS | 5 | |
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| 35. | A Framework for Developing Deep Learning Classification Models | 5 | |
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| 36. | XLNet: Generalized Autoregressive Pretraining for Language Understanding | AISC | 5 | |
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| 37. | Applications of Blockchain to IoT Security | AISC | 5 | |
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| 38. | Genomics with Deep Learning: A Concise Overview | AISC | 5 | |
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| 39. | Why you should be part of AISC community! | 5 | |
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| 40. | Paper Explained : PEGASUS, a SOTA abstractive summarization model by Google | AISC | 5 | |
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| 41. | AlphaFold 2, Is Protein Folding Solved? | AISC | 5 | Let's Play |
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| 42. | [AlphaGo Zero] Mastering the game of Go without human knowledge | TDLS | 4 | Let's Play |
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| 43. | Explainable AI with Layer-wise Relevance Propagation (LRP) | 4 | |
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| 44. | What is Wrong with Explainable AI? | AISC | 4 | |
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| 45. | Deep InfoMax: Learning deep representations by mutual information estimation and maximization | AISC | 4 | Guide |
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| 46. | Building AI Products; The Journey | Overview | 4 | |
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| 47. | Graph Neural Networks, Session 6: DeepWalk and Node2Vec | 4 | |
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| 48. | BERT & NLP Explained | 4 | Let's Play |
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| 49. | [StyleGAN] A Style-Based Generator Architecture for GANs, part2 (results and discussion) | TDLS | 4 | Discussion |
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| 50. | Modeling Dissolution of Compact Planetary Systems | 4 | |
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| 51. | The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words & Sentences From Natural Supervision | 4 | |
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| 52. | Logeo: Automatically Transform 2D Designs to 3D | 4 | Vlog |
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| 53. | Investigating Anti-Muslim Bias in GPT-3 | 4 | |
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| 54. | Neural Ordinary Differential Equations - part 1 (algorithm review) | AISC | 4 | Review |
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| 55. | News Recommender System Considering Temporal Dynamics and News Taxonomy | AISC | 4 | |
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| 56. | Explainable AI for Time Series - Literature Review | AISC | 3 | Review |
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| 57. | Similarity of neural network representations revisited | 3 | |
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| 58. | Machine Learning on Source Code - GitHub / Open AI Copilot | 3 | |
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| 59. | Deep learning enables rapid identification of potent DDR1 kinase inhibitors | AISC | 3 | |
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| 60. | Meta-Graph: Few-Shot Link Prediction Using Meta-Learning | AISC | 3 | |
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| 61. | Attention is not not explanation + Character Eyes: Seeing Language through Character-Level Taggers | | 3 | |
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| 62. | An overview of task-oriented dialog systems | AISC | 3 | Vlog |
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| 63. | Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches | | 3 | |
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| 64. | [FFJORD] Free-form Continuous Dynamics for Scalable Reversible Generative Models (Part 1) | AISC | 3 | |
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| 65. | Why do large batch sized trainings perform poorly in SGD? - Generalization Gap Explained | AISC | 3 | |
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| 66. | Multi-agent LLMs Course #business #startup https://maven.com/forms/30a683 | 3 | |
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| 67. | Review Nuggets - Mining Insight from Consumer Product Reviews | Workshop Capstone | 3 | Review |
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| 68. | Reinforcement Learning in the Real World (with Professor Matthew Taylor) | 3 | |
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| 69. | Pink Diamond - Data Driven Prediction of Venture Success | Workshop Capstone | 3 | |
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| 70. | Explainable AI, Session 5: Intro to SHAP | 3 | |
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| 71. | Using unsupervised machine learning to uncover hidden scientific knowledge | AISC | 3 | |
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| 72. | [OpenAI GPT2] Language Models are Unsupervised Multitask Learners | TDLS Trending Paper | 3 | |
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| 73. | Code Review: Transformer - Attention Is All You Need | AISC | 3 | Review |
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| 74. | Visualizing and measuring the geometry of BERT | AISC | 3 | |
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| 75. | [Original Style Transfer] A Neural Algorithm of Artistic Style | TDLS Foundational | 3 | |
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| 76. | [XAI] Explainable AI in Retail | AISC | 2 | |
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| 77. | Climate Risk Exposure Analysis Machine | 2 | |
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| 78. | Layerwise Learning for Quantum Neural Networks | AISC | 2 | |
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| 79. | DeSci Labs: Creating A Backend For Scientific Papers - Deep Random Talks S2 E1 | 2 | |
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| 80. | Data-Driven Behavior Change and Personalization - DRT S2E10 | 2 | |
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| 81. | Symmetries in Deep Learning - Deep Random Talks - Episode 18 | 2 | |
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| 82. | How do Aggregate Intellect Machine Learning Product Competitions work? | 2 | |
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| 83. | Women In AI Spring 2021 Showcase!!! | 2 | |
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| 84. | The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies | 2 | |
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| 85. | Interpretable Neural Networks for Panel Data Analysis in Economics | AISC | 2 | |
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| 86. | Learning Discrete Structures for Graph Neural Networks | AISC | 2 | |
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| 87. | High Dimensional Inference in the Universe | 2 | |
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| 88. | Building (AI?) Products; Step by Step Guide | AISC | 2 | Guide |
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| 89. | AI, Democracy, & Disinformation | 2 | Guide |
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| 90. | Tobias Pfaff (DeepMind): Learning to Simulate Complex Physics with Graph Networks | 2 | |
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| 91. | [RecSys 2018 Challenge winner] Two-stage Model for Automatic Playlist Continuation at Scale |TDLS | 2 | |
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| 92. | TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing | AISC | 2 | Guide |
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| 93. | Neural Image Caption Generation with Visual Attention (algorithm) | AISC | 2 | |
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| 94. | MLOps: Overview of Machine Learning Operations on the Cloud | AISC | 2 | |
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| 95. | Plug and Play Language Models: A Simple Approach to Controlled Text Generation | AISC | 2 | |
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| 96. | 'Less Than One'-Shot Learning (author speaking) | 2 | |
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| 97. | Azure MLops- Experiment Reproducibility Hands-on I- Session II, part 2 | 2 | |
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| 98. | Graph Neural Networks, Session 2: Graph Definition | 2 | |
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| 99. | Steve Brunton: Machine Learning for Fluid Dynamics | 2 | |
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| 100. | Machine Learning and Optimization - Deep Random Talks - Episode 17 | 2 | |
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