Tag
A free comprehensive step-by-step projects roadmap for becoming an AI researcher, covering topics from tokenizers to full capstone model systems.
This paper presents a formal roadmap for transitioning from late-fusion multimodal approaches to native multimodal modeling (NMM) within a unified transformer framework, categorizing existing models by input-output duality and systematically addressing architectural coordination, data curation, training recipes, and evaluation.
A free, open-source AI engineering curriculum that covers math, LLMs, and agents across 20 phases and 435 lessons in Python, TypeScript, Rust, and Julia, designed to fill gaps in fragmented AI tutorials.
This paper surveys the capabilities and limitations of AI across the full research lifecycle, from idea generation to dissemination, identifying a sharp boundary between reliable assistance and unreliable autonomy. It provides a taxonomy, benchmark suite, tool inventory, and design principles for human-governed AI collaboration in research.
A detailed 12-stage roadmap for becoming a Generative AI Engineer in 6 months, covering Python async, multimodal LLMs, RAG, agentic workflows, production deployment, MLOps, and safety, emphasizing building over tutorials.
A comprehensive, open-source GitHub repository providing structured learning roadmaps and curated resources for mastering AI, machine learning, deep learning, and large language models from beginner to advanced levels. Designed for students and professionals, it covers foundational concepts, programming frameworks, career tracks, and emerging AI topics.
A commerce beginner seeks a step-by-step roadmap and tool recommendations to automate web-to-PDF-to-Excel workflows plus AI-driven Excel formulas without coding experience.