2026 Top 10 Learning Resources for Large Language Models (LLM)

With the rapid development of artificial intelligence (AI) technology, especially in the fields of large language models (LLM) and agentic AI, how to effectively learn and master these technologies has become a hot topic for many developers and researchers. This article will recommend the top 10 learning resources worth paying attention to in 2026, helping you to go from basic entry to a deep understanding of the principles and applications of large models and agents.

Introduction

Large language models (LLM) are increasingly becoming core technologies across various industries, with their powerful natural language processing capabilities and knowledge reasoning abilities enabling automation and intelligence in multiple scenarios. Whether you want to start learning from scratch or wish to enhance your understanding and application skills in this technology, the following resources can provide you with practical support.

Recommended Resource List

1. LLM Basic Introduction Course

• Core Functionality: Provides a comprehensive introduction to the basic concepts, structure, and working principles of large models.
• Applicable Scenarios: Suitable for beginners, helping to build a preliminary understanding of large models.
• Pros and Cons:
  • Pros: Systematic content that is easy to understand.
  • Cons: May be slightly too simple for learners with some foundation.

2. Implementing LLM from Scratch Tutorial

• Core Functionality: Detailed explanation of how to implement a simple LLM from scratch, including code examples and step-by-step interpretations.
• Applicable Scenarios: Aimed at developers and researchers who want to get hands-on experience.
• Pros and Cons:
  • Pros: Practice-oriented and easy to get started.
  • Cons: The implementation process is too basic and does not cover complex applications.

3. In-depth Analysis of Agentic AI Technology

• Core Functionality: Explores the latest developments in agentic AI and studies how to enhance LLM applications through agents.
• Applicable Scenarios: For technical personnel looking to combine LLM with agents.
• Pros and Cons:
  • Pros: Cutting-edge content that is professional.
  • Cons: Strongly academic, may require foundational knowledge for better understanding.

4. Video Series: From LLM to Agent

• Core Functionality: Through lectures and case analyses, it delves into the applications of large models and the process of transitioning to agents.
• Applicable Scenarios: Visual learners who prefer to gain knowledge through videos.
• Pros and Cons:
  • Pros: Vivid and easy to absorb.
  • Cons: Slower learning pace, high requirement for self-directed learning.

5. AI Seminars and Community

• Core Functionality: Provides a platform for communication, gathering AI industry experts to discuss the latest research results and technologies.
• Applicable Scenarios: Researchers looking to exchange ideas and obtain cutting-edge information.
• Pros and Cons:
  • Pros: Resource sharing promotes brainstorming.
  • Cons: Participation requires time, and the information may be overwhelming.

6. Journals Related to Deep Learning

• Core Functionality: Includes the latest research papers and technical reports covering the fields of LLM and agents.
• Applicable Scenarios: Academic researchers seeking the latest research dynamics.
• Pros and Cons:
  • Pros: Strong academic nature and authoritative content.
  • Cons: Reading difficulty is high and requires some background knowledge.

7. Open Source Practice Projects

• Core Functionality: Provides multiple open-source LLM-related project source codes that can be used directly or further developed.
• Applicable Scenarios: Developers who want to practice hands-on, especially suitable for beginners.
• Pros and Cons:
  • Pros: Clear code demonstrations allow for free exploration.
  • Cons: May lack detailed documentation.

8. Online Course Platforms (e.g., Coursera, edX)

• Core Functionality: Aggregates numerous online courses related to AI and LLM offered by renowned universities and institutions.
• Applicable Scenarios: Users needing a systematic learning path.
• Pros and Cons:
  • Pros: Rich course offerings with a wide audience.
  • Cons: Some courses are paid, and the learning pace may be slow.

9. Recommended Reading List for AI Books

• Core Functionality: Recommends various books related to large models and deep learning, suitable for in-depth study.
• Applicable Scenarios: Those who prefer books and need systematic knowledge.
• Pros and Cons:
  • Pros: Strong systematic approach, convenient for in-depth research.
  • Cons: Reading experience may be lengthy, not suitable for quick information retrieval.

10. Technical Blogs and Forums

• Core Functionality: Summarizes the technical frontiers and application cases from multiple AI experts, sharing practical experiences.
• Applicable Scenarios: Developers who wish to learn through case studies and reference others' experiences.
• Pros and Cons:
  • Pros: Rich content with wide coverage.
  • Cons: Irregular updates and varying quality.

Summary Recommendation

The above 10 resources cover multiple dimensions from basic theory to practical application, allowing learners at different levels to find suitable study materials. Whether you are just starting to engage with large models or wish to delve deeper into their potential, these resources will provide you with practical information and support. I hope this article can assist you on your learning journey, enabling you to go further in the world of LLM and agents.