If you’re diving into LLMs or just curious about how tools like ChatGPT work under the hood, Karpathy’s breakdown is one of the clearest and most accessible resources out there. The following are highlights along with time stamps. Introduction (0:00 - 1:04) What are LLMs? Karpathy begins by explaining what large language models (LLMs) are, highlighting their capabilities and limitations. He also sets the goal of the session: to provide a comprehensive understanding of LLMs for a general audience.

This post continues our series on AI and ML. The aim is to enhance understanding of the main types of machine learning, explain the reasons for choosing the appropriate ML approach, and describe the data requirements for each method. Ways to learn There are three main ways of learning in machine learning. Supervised learning Unsupervised learning Reinforcement learning How would one choose an appropriate learning method? Appropriate ML method Selecting the appropriate machine learning method depends on your objectives.

Earlier this week, I participated in a two-day, hands-on introductory course on Machine Learning and AI, which was useful for someone new to the field like myself. The course helped me understand various concepts I had been curious about, including: The relationship between Machine Learning and AI. The definition of a “model” in this context and the process of “training” it. Clarification on terms like “Hugging Face” and “Llama.” The importance of quality data in machine learning projects.