I created this repo to have an organized view of them in my mind. I might not need this in future, or maybe I may need to create more, for now let's start with this.
- ML References
- AWS Certified Machine Learning Specialty (Udemy Course Notes)
- AWS Certified Cloud Practitioner (AWS Course Notes)
- 2025-3-23 | Confidence Intervals for Regression Models : A comparison of methods of computing confidence intervals for regression models
- 2025-3-14 | DeepSeek : Summary documentation containing history of DeepSeek models and various URLs
- 2025-3-6 | Deep Dive into LLMs like ChatGPT : my takeaways from the video
- 2025-3-6 | DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters : my takeaways from the video
- 2024-12-08 | Advancing AI with Local Learning and Uncertainty Estimation : o1-preview summary of the video
- 2024-11-24 | Stanford CS229 I Machine Learning I Building Large Language Models (LLMs) : o1-preview summary of the video
- 2024-05-05 | CLIP - Contrastive Language-Image Pretraining : A brief explanation of CLIP method
- 2023-12-22 | Approximate Nearest Neighbor Methods : This blog post explores Approximate Nearest Neighbor (ANN) methods, discussing their importance, various techniques like HNSW, LSH, ANNOY, and Spill Trees, and Python libraries for implementing these methods.
- 2023-12-18 | Text Classification Using Class Information : How should our approach to text classfication change if our classes also have meanings.
- 2023-12-13 | Model Deployment Strategies : I dissect the machine learning model development lifecycle, exploring deployment strategies and techniques for effective model deployment.
- 2023-08-26 | Alignment Problem : Summary of the blog Musings on the Alignment Problem by Jan Leike
- 2022-08-25 | MFCC : Short description for MFCC
- 2023-08-25 | Hypothesis Testing : Short description for hypothesis testing
- 2023-08-25 | z-score : Short description for z-score
- 2021-10-16 | Edward Hu et. al. | LoRA: Low-Rank Adaptation of Large Language Models : LoRA introduces a resource-efficient approach for adapting large pre-trained language models, such as GPT-3, to specific tasks without the heavy costs of traditional fine-tuning. It maintains model quality, minimizes inference latency, and facilitates quick task-switching.
- 2023-12-19 | Lingling Xu et. al. | Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment : This paper critically reviews Parameter Efficient Fine-Tuning (PEFT) methods for large pretrained language models (PLMs), highlighting their benefits in resource-limited settings. It assesses these methods' performance, efficiency, and memory usage across tasks like natural language understanding, machine translation, and generation.
- 2023-05-17 | Rohan Anil et. al. | PaLM 2 Technical Report : We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM.
- 2023-05-18 | Chunting Zhou et. al. | LIMA: Less Is More for Alignment : Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages.
- 2022-03-29 | Jordan Hoffmann et. al. | Training Compute-Optimal Large Language Models : We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.