With 23 high initiatives, 96 subprojects, and 6000 traces of Python code, this vendor-neutral coursebook is a goldmine for any analytic skilled or AI/ML engineer concerned about growing superior GenAI or LLM enterprise apps utilizing ground-breaking know-how.

This isn’t one other ebook discussing the identical matters that you just study in bootcamps, school courses, Coursera, or at work. As an alternative, the main focus is on implementing options that deal with and repair the principle issues encountered in present functions. Utilizing foundational redesign fairly than patches comparable to immediate engineering to repair backend design flaws.

You’ll learn to shortly implement from scratch functions truly utilized by Fortune 100 firms, outperforming OpenAI and the likes by a number of order of magnitudes, by way of high quality, velocity, reminiscence necessities, prices, interpretability (explainable AI), safety, latency, and coaching complexity.

Content material

With tutorials, enterprise-grade initiatives with options, and real-world case research, this coursebook covers state-of-the-art materials on GenAI, generative adversarial networks (GAN), knowledge synthetization, and rather more, in a compact format with many latest references. It consists of deep dives into modern, ground-breaking AI applied sciences such a xLLMs (excessive LLMs), invented by the writer.

The main target is on explainable AI with sooner, less complicated, high-performance, automated algorithms. As an example: NoGAN, new analysis metrics, xLLM (self-tuned multi-LLM based mostly on taxonomies with software to clustering and predictive analytics), variable-length embeddings, producing observations outdoors the coaching set vary, quick probabilistic vector search, or Python-generated SQL queries. The writer additionally discusses alternate options to conventional strategies, for example to synthesize geospatial knowledge or music.

This textbook is a useful useful resource to instructors and professors educating AI, or for company coaching. Additionally, it’s helpful to arrange for job interviews or to construct a sturdy portfolio. And for hiring managers, there are many authentic interview questions. The quantity of Python code accompanying the options is appreciable, utilizing an unlimited array of libraries in addition to home-made implementations displaying the internal workings and enhancing present black-box algorithms. By itself, this ebook constitutes a stable introduction to Python and scientific programming. The code can be on GitHub.

Find out how to Get Your Copy?

Printed in Might 2024 by GenAItechLab.com, 206 pages. Contains two glossaries (GenAI and LLM), an index, fashionable bibliography, dozens of illustrations and tables, and varied clickable references each inside and exterior. Simple to browse in Chrome, Edge or any PDF viewer.  See desk of contents, right here.

Purchase the ebook on our e-Retailer, right here.

Writer

Vincent Granville is a pioneering GenAI scientist and machine studying professional, co-founder of Knowledge Science Central (acquired by a publicly traded firm in 2020), Chief AI Scientist at MLTechniques.com and GenAItechLab.com, former VC-funded government, writer (Elsevier) and patent proprietor — one associated to LLM. Vincent’s previous company expertise consists of Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. Comply with Vincent on LinkedIn.