Tag
A curated stack of 20 books across 5 categories for learning AI/ML from fundamentals to production systems.
A Principal Engineer at Atlassian shares a completed 25-day Twitter thread series on system design scaling fundamentals, compiled into a single resource.
A tweet recommends a series of explainer articles on LLM fundamentals, particularly 'Vectors are all you need', adding to a list of top learning resources.
A tweet from @Franc0Fernand0 advises engineers to master 12 fundamental data structures and algorithms topics before jumping into LeetCode, suggesting one topic per week.
A comprehensive blog series by Rahul (MLWhiz) builds an understanding of LLMs from scratch, covering transformers, pretraining, and post-training in five parts.
An article explaining the fundamentals of computing and software from first principles, aiming to demystify how computers work for a general audience.
A tweet shares a link to an article about the fundamentals of GPU architecture.
A beginner-friendly GitHub repository covering PyTorch fundamentals, including tensor initialization, operations, indexing, and reshaping, with over 900 stars.
A tweet shares a curated list of 50 LLM interview questions covering fundamentals, fine-tuning, generation, advanced concepts, and math, compiled by Hao Hoang.
Aaron Levie argues that despite AI's capabilities, students and professionals should not abandon learning the fundamentals of their domains, as experts who deeply understand their craft will be far more effective with AI tools than novices.
An introductory article on systems programming covering fundamentals such as bit manipulation, parsing, filesystems, syscalls, and memory management, targeted at programmers.
Discusses how AI amplifies code quality, emphasizing that software fundamentals matter more than ever, and recommends five design patterns for building reliable AI agents.