aishwaryanr / awesome-generative-ai-guide

2024-02-06 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, medium

Summary

The "aishwaryanr/awesome-generative-ai-guide" GitHub repository serves as a comprehensive, regularly updated resource hub for generative AI. It compiles a wide array of materials, including monthly lists of top GenAI research papers, extensive interview preparation resources, and numerous code notebooks for developing generative AI applications. The guide features several course materials, such as the 10-week "Applied LLMs Mastery 2024" which began January 15, 2024, "Generative AI Genius 2024," and certified courses like "AI Evals for Everyone" and the newly launched "OpenClaw Mastery for Everyone." Additionally, it provides a curated list of over 90 free GenAI courses covering LLM basics, application building, prompt engineering, RAG, fine-tuning, evaluation, multimodal models, and AI agents. The repository also highlights a "Top AI Tools List" and includes security tools like OWASP Agent Memory Guard.

Key takeaway

For Machine Learning Engineers or AI Scientists seeking to deepen their generative AI expertise or prepare for interviews, you should leverage the "aishwaryanr/awesome-generative-ai-guide" repository. This hub provides structured learning paths, including the "Applied LLMs Mastery 2024" course, over 90 free GenAI courses, and specific interview preparation materials. Utilize its code notebooks for practical application development and stay updated on the latest research and tools to enhance your skill set effectively.

Key insights

This repository centralizes diverse generative AI learning and development resources.

Principles

• Continuous learning is essential in rapidly evolving GenAI.
• Structured roadmaps accelerate skill development.
• Comprehensive resources support varied learning paths.

In practice

• Explore 90+ free GenAI courses.
• Utilize interview prep questions.
• Review RAG and fine-tuning notebooks.

Topics

• Generative AI Resources
• Large Language Models
• LLM Application Development
• Prompt Engineering
• Retrieval-Augmented Generation
• AI Agents

Code references

• aishwaryanr/awesome-generative-ai-guide
• microsoft/generative-ai-for-beginners
• aws-samples/amazon-bedrock-workshop
• gkamradt/langchain-tutorials
• ray-project/llm-applications

Best for: AI Student, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.