New Book: Building Disruptive AI & LLM Technology from Scratch
Available here. This book features new advances in game-changing AI and LLM technologies built by GenAI Techlab. Written in simple English, it is best suited for engineers, developers, data scientists, analysts, consultants and anyone with an analytic background interested in starting a career in AI. The emphasis is on scalable enterprise solutions, easy to implement, yet outperforming vendors both in term of speed and quality, by several orders of magnitude.
Each topic comes with GitHub links, full Python code, datasets, illustrations, and real-life case studies, including from Fortune 100 company. Some of the material is presented as enterprise projects with solution, to help you build robust applications and boost your career. You don’t need expensive GPU and cloud bandwidth to implement them: a standard laptop works.
Published in October 2024, 193 pages. Includes glossary, index, bibliography, dozens of illustrations and tables, and various clickable references both internal and external. Easy to browse in Chrome, Edge or any PDF viewer.?
About the author
Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist at?ML Techniques?and?GenAI Techlab, former VC-funded executive, author (Elsevier) and patent owner — one related to LLM. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET.
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1 个月"neuromorphic Computing"?-->..... A brandnew articles. Leave a??LIKE??or COMMENT OR QUESTION ON : English : https://aifornoobsandexperts.com/neuromorphic-computing/ Dutch :?https://aivoorjanenalleman.nl/neuromorphic-computing/
Co-Founder at Bart?n Blockchain Community | Data Scientist | Blockchain Researcher
1 个月Given the intricacies of this text processing flowchart, I’m curious about how this system handles languages with agglutinative characteristics, where words are formed by extensive suffixation. What specific methods or improvements have been implemented to enhance the effectiveness and accuracy of the system when processing such languages? What are the major challenges encountered, and what strategies are employed to overcome them?