๐ธ Additional Resources
Feel free to contact us at [email protected]
๐งฐ Toolsโ
๐ Blogโ
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Learn how to fine-tune Llama-3-8b on the Together AI platform using your proprietary data to create a custom model that outperforms Llama-3-70b and competes with leading closed-source alternatives like GPT-4.
๐ Booksโ
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Written by @rasbt and open-sourced, "LLMs-from-scratch" is a practical guide to building an LLM from scratch. It has quickly gained 21.9K stars and covers tuning an LLM from scratch, including modifying Prompt formats, instruction masks, or LoRA, making it a complete and readable book!
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41 pages, 3 modules, 11 chapters of content on starting a career in AI/LLM. Andrew Ng's new book "How to Build a Career in AI" is now available!๐ฅ
The content includes:
- What to learn to get an AI job?
- Practical projects to quickly grasp core AI knowledge?
- How to prepare for an AI job?
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A comprehensive guide to developments and system knowledge in the GenAI/LLM field since ChatGPT's release, divided into 9 parts. It references top blogs, papers, YouTube, and online courses to provide a clear understanding of GenAI's evolution!
- A2 English for Developers (Beta)
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This course is designed for beginners, aligned with the CEFR A2 level. The course content includes:
- Navigating the First Day at Work: Learn essential workplace communication skills for scenarios like self-introductions, familiarizing with colleagues, asking for lunch suggestions, and requesting access cards from security.
- Self-Introduction: Master the art of introducing yourself professionally, including your job role, and sharing personal goals in team meetings.
- Everyday Conversations: Develop the ability to talk about your hobbies and personality, discover local attractions and restaurants, and describe your daily work routine and tasks. Learn how to effectively share these details with others.
- Project Presentation: Learn how to present your projects, clearly explaining your role and contributions.
- Discussing Hot Tech Topics: Enhance your skills in discussing current technology trends and topics.
- Programming Terminology: Understand and appropriately use various programming-related professional terms in daily conversations.
- Project Documentation: Gain expertise in writing clear and comprehensive project documentation.
- Project Updates: Learn how to present the latest updates and future plans for your projects effectively.
Recommendation Rating ๐ ๐ ๐ ๐ Generative AI Cookbook by Databricks This comprehensive Generative AI Cookbook by Databricks offers both theoretical knowledge and hands-on experiments. The initial edition focuses on Retrieval-Augmented Generation (RAG). Future updates will include sections on Agents & Function Calling, Prompt Engineering, Fine Tuning, and Pre-Training.
Recommendation Rating ๐๐๐๐ MIT AI Lab's 130-page Machine Learning Checklist Highly recommended by the MIT AI Lab, this 130-page Machine Learning checklist is a must-have resource for understanding core #MachineLearning concepts. With a current open-source status and 6.7K stars on GitHub, it covers a wide range of topics including probability, generative models, Gaussian models, Bayesian statistics, linear regression, logistic regression, the EM algorithm, kernels, Monte Carlo inference, and deep learning.
๐งโ๐ซ Coursesโ
- Karpathy's LLM101n Course
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Andrej Karpathy, former OpenAI co-founder and Tesla AI team lead, is launching an AI education company - Eureka Labs. Karpathy says Eureka Labs will be an AI-native school, with his work at Tesla and OpenAI being his full-time passion and teaching as a side task. The first product, LLM101n, promises to be an undergraduate-level course teaching students to build their own AI assistants.
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Parlance Lab's RAG series of courses, from basic knowledge to practical experience, in video format with demo files, is highly recommended.
> Back to Basics for RAG
Instructor: Jo Bergum
Covers basic knowledge of information retrieval (IR) and failure modes of vector embeddings in retrieval, with practical solutions to avoid them. Shows how to set up simple but effective IR evaluations for your data, allowing faster exploration and systematic methods to improve retrieval accuracy.
>> Beyond the Basics of RAG
Instructor: Ben Claviรฉ
Implementing RAG effectively is more complex than it seems. This course explores how to build a robust RAG process and how simple insights from retrieval research can significantly improve your RAG efforts. Topics include BM25, re-ranking, indexing, domain specificity, and more.
>>> Systematically Improving RAG Applications
Instructor: Jason Liu
This course introduces methods that anyone can apply to improve their RAG applications!
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How to build an "end-to-end, production-level LLM, RAG, and LLMOps system"?
- Data: Social data -> ETL -> MongoDB
- Features: Bytewax cleaning, splitting, vectorizing data, storing in qdrant_engine
- Training: QLora fine-tuning, Cometml experiment tracking
- Inference: Deploying AWS to build RAG systems
