You could call it the best kept secret for professionals and experts in AI, as you won’t find these books and articles in traditional outlets. Yet, they are read by far more people than documents posted on ArXiv or published in scientific journals, so not really a secret. Actually, one of these books is also published by Elsevier (here), but this is the exception rather than the rule.
What makes them different? They are written by an expert with considerable practical experience and successful, large-scale enterprise implementations of the material discussed. While covering state-of-the-art with numerous ground-breaking innovations and enterprise-grade Python code, on occasions covering topics well beyond PhD level, the focus is on explaining efficient technology and better mouse traps, in simple English without jargon.
Articles
- Doing Better with Less: LLM 2.0 for Enterprises
- How to Design LLMs that Don’t Need Prompt Engineering
- Blueprint: Next-Gen Enterprise RAG & LLM 2.0 – Nvidia PDFs Use Case
- 10 Tips to Design Hallucination-Free RAG/LLM Systems
- The Rise of Specialized LLMs for Enterprise
- From 10 Terabytes to Zero Parameter: The LLM 2.0 Revolution
- Key LLM 2.0 Concepts Explained in Simple English
- Universal Dataset to Test, Enhance and Benchmark AI Algorithms
- What is LLM 2.0?
Books
- Building Disruptive AI & LLM Technology from Scratch
- State-of-the-Art GenAI and LLMs Creative Projects, with Solutions
The second one is popular among data scientists and AI engineers who want to learn efficient state-of-the-art technology discussed nowhere else, at a fraction of the cost and time commitment required by bootcamps and traditional training.
For more books, see here.
Other resources
See my PowerPoint presentation about xLLM, accessible from here. There is another one about NoGAN — the best tabular data synthesizer with best evaluation metric — posted here. For open-source code, see our GitHub repository, here.
We also offer an AI Fellowship with a certification that you can add to your credentials section on your LinkedIn profile, for under $100. Based on the same material. See details here.
More articles are posted on the following blogs:
For deep technical research papers, see here.
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