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RESEARCH
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Essential Resources for Data Science ML AI and LLM Learning
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End of this thread! If you are looking to learn more about
Data Science
ML/DL/AI
Analytics
Math & Statistics
Resources
LLMs
MLOps Then, Don't forget to follow me at @avikumart_ for upcoming posts -
Planning Methods and World Models for Robotic Grasping Tasks
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As I have argued, the winning combination would be planning-based methods using trained world models.
But with such simple kinematics/dynamics and relatively simple grasping tasks, trainable world models are superfluous. -
Essential LLM and GenAI Courses Collection for Learning
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Choose from a collection of courses to learn more about LLMs and related applications https://
github.com/avikumart/LLM-
GenAI-Transformers-Notebooks/blob/main/LLM-GenAI-Courses.md
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DeepLearning.AI Releases New Course on RLHF Fine-tuning
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Deeplearning .AI has released a new course on RLHF using google pipeline which can be useful to gain deeper insights about RLHF and fine-tuning using the same. Check the course here and sign up to learn more
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RLHF: Aligning LLMs with Human Values and Preferences
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Reinforcement Learning from Human Feedback (RLHF) is currently the main method for aligning LLMs with human values and preferences. RLHF is also used for further tuning a base LLM to align with values and preferences that are specific to your use case.
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Aligning Large Language Models with Human Values via RLHF
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Large language models (LLMs) are trained on human-generated text, but additional methods are needed to align an LLM with human values and preferences. To align models with human values and individual RLHF is a highly recommended solution
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Distance-Based Methods: Supervised vs Unsupervised Learning
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Both are based on distance based methods but one is supervised while the other is unsupervised algorithm
Well explained -
Machine Learning Research: Room for Niche Ideas in High-Dimensional Space
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i used to find this kind of thing intimidating – ML is so popular, competitive, etc
— dr. jack morris (@jxmnop) 16 décembre 2023
but I realized how different every paper is. each presenter here traveled down one single esoteric rabbit hole
high-dimensional spaces are so sparse; there's still plenty of room for your ideas https://t.co/U7jR3OefBJi used to find this kind of thing intimidating – ML is so popular, competitive, etc but I realized how different every paper is. each presenter here traveled down one single esoteric rabbit hole high-dimensional spaces are so sparse; there's still plenty of room for your ideas
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Beyond Language: Why GPT-4 Needs Training Data
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long story short, my task can't be completed by GPT-4; the data isn't only language, it includes continuous vectors, so you really need training data for this