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Tác giả: Denis Rothman
NXB: Packt Publishing
Chi tiết sản phẩm
Follow authors to get new release updates, plus improved recommendations. My core belief is that you only truly understand something when you can teach it. That principle shaped my early years studying at Sorbonne University and Paris Cité University, where I also assisted with teaching while developing my first ideas in natural language processing. During that period, I created and registered early patents in word tokenization, encoding systems, and conversational human‑machine interaction. After those beginnings, I spent more than three decades deploying real‑world AI systems for global companies across aerospace, manufacturing, luxury, and logistics. This included cognitive NLP chatbots, enterprise optimization engines, and advanced planning and scheduling solutions used worldwide. From the start, I treated explainability as essential, integrating interpretable, acceptance‑based interfaces into industrial AI systems long before XAI became a formal field. Sharing knowledge has always been central to my work. I write books that translate hands‑on AI experience into practical algorithms, frameworks, and architectures. I remain model‑ and platform‑agnostic, always showing multiple ways to solve a problem. As a full‑stack developer, I believe in teaching through code, so I publish complete programs on GitHub as proof-of-concept. Read more about this author Read less about this author Read more about this author Read less about this author
About the AuthorDenis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
Editorial ReviewDenis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
Thông tin sách: Building Business-Ready Generative AI Systems: Build Human-Centered AI Systems with Context Engineering, Agents, Memory, and LLMs for Enterprise (Kindle, 444 trang) – Packt Publishing, 2025. Ngôn ngữ: Tiếng Anh.
Supercharge your business with context-aware AI controllers, adaptive agents, multimodal reasoning functionality, neuroscientific memory systems, and flexible handler mechanisms that integrate the emerging generative AI models.
Get with your book: PDF copy, AI Assistant, and Next-Gen Reader free.
Key FeaturesStandalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build agentic, ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context engineering.
You'll design AI controller architectures with multi-user memory retention to dynamically adapt your system to diverse user and system inputs. You'll architect a Retrieval-Augmented Generation system with Pinecone to combine instruction-driven scenarios. Through context engineering, you’ll minimize token usage, maximize response quality, and create systems that reason across complex tasks with precision. You'll enhance your system’s intelligence with multimodal capabilities—image generation, voice interactions, and machine-driven reasoning—leveraging Chain-of-Thought and context chaining to address cross-domain automation challenges. You'll also integrate OpenAI’s suite and DeepSeek-R1 without disrupting your existing GenAISys ecosystem.
With context engineering as the backbone, every step becomes a deliberate act of shaping model behavior. Your GenAISys will apply neuroscience-inspired insights to marketing strategies, predict human mobility, integrate smoothly into human workflows, and connect to live external data, all wrapped in a polished, investor-ready interface.
What you will learnThis book is for AI and Machine Learning Engineers seeking to enhance their understanding of Generative AI and its enterprise applications. It will particularly benefit those interested in building AI agents, creating advanced orchestration systems, and leveraging AI for automation in marketing, production, and logistics. Software architects and enterprise developers looking to build scalable AI-driven systems will also find immense value in this guide. No prior superintelligence experience is necessary, but familiarity with AI concepts is recommended.
Table of ContentsGiá bán
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0Saurabh Shrivastava, Kamal Arora, Ashutosh Dubey, Dhiraj Thakur, Sanjeet Sahay
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0Maxim Lapan
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0Valentina Alto
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0Chris Fregly, Antje Barth, Shelbee Eigenbrode
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2.622.000 ₫
0Christoffer Noring
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0Denis Rothman
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0Jared Matfess
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0Ali Aminian, Hao Sheng
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0Salvatore Raieli, Gabriele Iuculano
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0Eickhel Mendoza
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Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning
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Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3
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5–16 ngày
XL 2 ngày + VC 3–14 ngày
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