Building and Training Large Language Models (LLMs): A Stanford Lecture SummaryThis is a summary of the Stanford CS229 Machine Learning lecture : Building Large Language Models (LLMs) by Yann DuboisApr 1Apr 1
Understanding Reasoning in LLMsThese notes are based on Sebastian’s article about Understanding Reasoning LLMs from his newsletter. It offers valuable insights explained…Feb 25Feb 25
Key Takeaways from the MLOps Data Lifecycle in Production CourseAs I revisit the Machine Learning Data Lifecycle in Production course (a refresher before diving into the third course), I’d like to share…Jan 15Jan 15
From Raw Data to Model Efficiency: Mastering Feature Engineering and SelectionIn the world of machine learning, your model is only as good as the data it learns from. Transforming raw data into a structured…Dec 6, 2024Dec 6, 2024
Enhancing Large Language Models: The Power of Continued Pre-Training𝐖𝐡𝐚𝐭 𝐢𝐬 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐞𝐝 𝐩𝐫𝐞-𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠Nov 17, 20241Nov 17, 20241
Reproducing GPT-2 124M: Key Insights from Andrej Karpathy’s 4-Hour Deep DiveThis is a summary of Andrej karpathy’s video about pre-training a GPT-2 124M parameter model from scratch.Feel free to check it using this…Sep 10, 2024Sep 10, 2024
Multi-Agent System with Crew AI: A Short Course SummaryPlease note that this article contains direct quotes content from the short course on multi-agent systems with Crew AI.Jul 4, 2024Jul 4, 2024
Stanford’s CS25: Lecture 2summaryLecture 2 of Stanford University’s CS25 V4 Transformers course was delivered by Jason Wei and Hyung Won Chung. Highly recommended to watch…Jul 2, 2024Jul 2, 2024
Stanford’s CS25: Lecture 1 summaryI recently watched the first lecture of Stanford University CS25 V4 Transformers course, and presented by Div Garg, Steven Feng, Emily…Jun 11, 2024Jun 11, 2024
𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐄𝐥𝐢𝐜𝐢𝐭𝐬 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠…Imagine a scenario where you’re listening to a story. As you follow along, your mind naturally starts to visualize the the unseen objects…May 24, 2024May 24, 2024
