Greater Quebec City Metropolitan Area
Read my just released 📖 The Hundred-Page Language Models Book: https://thelmbook.com Read my bestselling 📖 The Hundred-Page Machine Learning Book: https://themlbook.com Read 📖 Machine Learning Engineering: https://mlebook.com Subscribe to my weekly ✉️ Artificial Intelligence newsletter: https://www.linkedin.com/newsletters/artificial-intelligence-6598352935271358464/ and to my weekly ✉️ Data Science newsletter: https://www.linkedin.com/newsletters/7102511020270608384/ About me: Ph.D. in Artificial Intelligence, passionate about data, fluent in English, French, and Russian. Solid scientific programming and team leadership skills, with over 20 years of experience working on various computing projects, including several of my own startups. More than 15 years of hands-on experience in automated data analysis, machine learning, and natural language processing. Trained a Transformer from scratch and fine-tuned pretrained transformers for various tasks. Built a robot that crawls the internet, finds websites with business-critical information, and retrieves updated information periodically. Developed an enterprise chatbot that doesn’t hallucinate. Expert in Python and Java with several years of daily design and development experience in big data contexts. Specialties: machine learning, natural language processing, conversational interfaces (chatbots), information retrieval.
Building ChapterPal.
"This book cleared up a lot of conceptual confusion for me about how Machine Learning actually works—it is a gem of clarity. The worked examples and notebook applications gave me a solid starting point for exploration. Even if you are not planning a career in machine learning applications, this is a solid foundation for thinking about the capabilities of these unique new tools.” —Vint Cerf, Internet pioneer and Turing Award recipient "The book is a good start for anyone new to language modeling who aspires to improve on state of the art." —Tomáš Mikolov, the author of word2vec and FastText
“If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book.” —Cassie Kozyrkov, Chief Decision Scientist at Google “Foundational work about the reality of building machine learning models in production. Comes at the right time when companies start to see through the AI hype, realizing it takes a conscious engineering effort and best practices to make machine learning work. Another great book from Andriy!” —Karolis Urbonas, Head of Machine Learning and Science at Amazon Released on Amazon, B&N, and Leanpub. Drafts are available on http://www.mlebook.com.
Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field." Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."
MindsDB is focused on fascinating and, in my view, crucial aspect of applied AI: seamlessly integrating machine learning capabilities directly into the data layer. They empower developers to leverage existing databases as predictive engines, using the familiar language of SQL. This dramatically lowers the barrier to building AI-powered applications, abstracting away much of the traditional MLOps complexity. I mainly contributed by: - Helping in defining product features, making sure MindsDB continues to solve real-world developer problems with elegance and power. - Hosting webinars designed to clearly demonstrate the simplicity and versatility of MindsDB for software engineers.
Leading the team of Machine Learning developers digging through the gigabytes of talent marketplace data. Tools and techniques used: machine learning (deep/shallow, LLMs, classification, generative AI, clustering, topic modeling), regular expressions, Python, MySQL, scikit-learn, PyTorch.
Leading a team working on "hacking" text and other unstructured or semi-structured data to extract useful information. Problems successfully solved by me and my team include: — conversational user interfaces (chatbots); — parsing of free-form résumé to detect and extract candidate name, employers, skills, certifications, employment history; — parsing of job descriptions to detect and extract job title, salary, employer name, assign a standard occupation code; — automated noisy and multi-language data normalization, cleanup, deduplication; — novel data detection; — language detection / text segmentation by language; — sequence labeling (queries, addresses, salaries); — machine translation; — Web-data analysis. Tools and techniques used: machine learning (deep/shallow, classification, clustering, topic modeling, structured output prediction), regular expressions, Java, Python, MySQL, scikit-learn, Keras.