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AI2ML AI to Machine Learning

Original articles on artificial intelligence and machine learning, deep optimization. Less is more, life is simple! Shi Chunqi

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Latest from AI2ML AI to Machine Learning

57 recent articles

AI2ML AI to Machine Learning

Jun 6, 2025 · Artificial Intelligence

Tackling the Top Challenges of Retrieval‑Augmented Generation (RAG)

The article enumerates common pitfalls of Retrieval‑Augmented Generation—such as missing content, low‑rank document misses, context limits, format errors, incomplete answers, scalability bottlenecks, complex PDF extraction, data‑quality issues, domain adaptation gaps, hallucinations, and feedback‑loop deficiencies—and offers concrete mitigation strategies ranging from data cleaning and prompt design to hybrid search, hierarchical retrieval, document compression, and automated evaluation.

Hybrid SearchLLMRAG

0 likes · 9 min read

Tackling the Top Challenges of Retrieval‑Augmented Generation (RAG)

AI2ML AI to Machine Learning

Apr 17, 2025 · Artificial Intelligence

Inside Qwen: A Deep Dive into the Large Model’s Source Code

The article provides a comprehensive technical walkthrough of Qwen’s large‑model series, covering data preparation, tokenization, model tweaks, training settings, RLHF pipeline, Code‑Qwen specifics, Qwen2 and Qwen3 architectural changes, scaling‑law experiments, and detailed source‑code analysis with illustrative diagrams.

MoEQwenRLHF

0 likes · 7 min read

Inside Qwen: A Deep Dive into the Large Model’s Source Code

AI2ML AI to Machine Learning

Mar 21, 2025 · Artificial Intelligence

Comparing Four Leading Open‑Source LLM Agent Frameworks: Autogen, CrewAI, LangGraph, and Swarm

This article provides a detailed comparison of four prominent open‑source LLM agent frameworks—Autogen, CrewAI, LangGraph, and Swarm—covering their core concepts, strengths, weaknesses, ideal use cases, and how they differ in scalability, memory handling, tool integration, and community support.

AutoGenCrewAILLM agents

0 likes · 14 min read

Comparing Four Leading Open‑Source LLM Agent Frameworks: Autogen, CrewAI, LangGraph, and Swarm

AI2ML AI to Machine Learning

Feb 10, 2025 · Artificial Intelligence

Eight Ways Enterprises Can Leverage DeepSeek

The article outlines eight distinct enterprise strategies for adopting DeepSeek, categorizing them by model maturity, available data types, and specific business challenges, and maps these approaches onto four capability tiers—from basic compliance requirements to advanced multimodal, low‑cost solutions.

AI agentsDeepSeekRAG

0 likes · 3 min read

Eight Ways Enterprises Can Leverage DeepSeek

AI2ML AI to Machine Learning

Feb 8, 2025 · Artificial Intelligence

Analyzing DeepSeek R1 Inference Projects: Source Code, Cold‑Start, and Scaling Techniques

This article examines DeepSeek R1’s three breakthroughs, its low‑cost optimizations that bypass CUDA, and the resulting impact on the AI ecosystem, then provides a detailed technical review of seven open‑source reproductions—Open‑R1, Tiny‑Zero, SimpleScaling‑S1, and simpleRL‑reason—covering their architectures, reinforcement‑learning pipelines, and code implementations.

DeepSeekInference ScalingLarge Language Models

0 likes · 10 min read

Analyzing DeepSeek R1 Inference Projects: Source Code, Cold‑Start, and Scaling Techniques

AI2ML AI to Machine Learning

Feb 5, 2025 · Artificial Intelligence

What Optimizations Power DeepSeek’s High‑Efficiency LLMs?

The article enumerates DeepSeek’s extensive technical optimizations—including Grouped Query Attention, Multi‑head Latent Attention, Mixture‑of‑Experts, 4D parallelism, quantization, and multi‑token prediction—that together enable cheap, high‑performance large language models.

4D parallelismDeepSeekGrouped-Query Attention

0 likes · 8 min read

What Optimizations Power DeepSeek’s High‑Efficiency LLMs?

AI2ML AI to Machine Learning

Aug 3, 2024 · Artificial Intelligence

Why Chinese Universities Are Falling Behind in AI4Science Courses

The article analyzes the rapid rise of AI4Science, highlights the talent gap in China’s industrial sector, presents an AI‑driven PDE case study, compares the strengths and challenges of physics‑based versus data‑driven modeling, and surveys leading AI‑for‑Science courses at top Western universities, concluding that Chinese institutions still lag behind in curriculum development.

AI4ScienceCourse ComparisonPhysics-Informed Neural Networks

0 likes · 9 min read

Why Chinese Universities Are Falling Behind in AI4Science Courses