Writers machine learning (8 resultados)
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 33,03
Envío por EUR 2,27Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 36,76
Envío por EUR 2,27Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 43,92
Envío por EUR 17,45Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 44,91
Envío por EUR 17,45Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 45,54
Envío por EUR 30,50Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. Neuware - Hands-On AI Engineering covers both the construction of LLM systems and the core challenges modern AI teams face daily: performance limits, reliability, evaluation, and cost control.Written by 4 practicing AI engineers. Inside you'll learn how to design, build, and operate LLM systems that…run efficiently, scale, and perform under pressure. Using local, open-source tools, without expensive cloud credits or black-box APIs.What's included.- Training and Fine-Tuning Neural Networks with PyTorch. A set framework on how to effectively train and adapt models using PyTorch.- Parameter-efficient fine-tuning with LoRA and QLoRA, and how these techniques make it practical to customize large models on consumer GPUs.- Building robust RAG pipelines through smart chunking, hybrid retrieval, intelligent ranking, and faithfulness/grounding mechanisms.- Proper evaluation methods including rubrics, LLM-as-a-judge, golden datasets, and regression testing for reliable assessment.- Production realities. Key insights into monitoring, guardrails, cost optimization, and reliable deployment of LLM systems.Performance add-ons (last chapter)A companion GitHub repository, carefully sequenced projects you can follow along with and build yourself.- Project 1 - Simple Companion Chat: Basic chatbot built around a single document.- Project 2 - Personal Knowledge Q&A: Ask questions over your own files with grounded answers.- Project 3 - Checked Q&A System: Compare AI answers against expected results.- Project 4 - Conversational Agent: Multi-turn chat with memory and simple tools.- Project 5 - Document Summarizer: Controlled summaries with basic quality checks.- Project 6 - Chapter Explorer: Turn text into outlines and short quizzes. These projects mirror modern team workflows and give you something concrete to show in interviews or client work.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 35,36
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Hands-On AI Engineering covers both the construction of LLM systems and the core challenges modern AI teams face daily: performance limits, reliability, evaluation, and cost control.Written by 4 practicing AI engineers. Inside you'll learn how to design, build, and operate LLM systems that r…un efficiently, scale, and perform under pressure. Using local, open-source tools, without expensive cloud credits or black-box APIs.What's included.Training and Fine-Tuning Neural Networks with PyTorch. A set framework on how to effectively train and adapt models using PyTorch.Parameter-efficient fine-tuning with LoRA and QLoRA, and how these techniques make it practical to customize large models on consumer GPUs.Building robust RAG pipelines through smart chunking, hybrid retrieval, intelligent ranking, and faithfulness/grounding mechanisms.Proper evaluation methods including rubrics, LLM-as-a-judge, golden datasets, and regression testing for reliable assessment.Production realities. Key insights into monitoring, guardrails, cost optimization, and reliable deployment of LLM systems.Performance add-ons (last chapter)A companion GitHub repository, carefully sequenced projects you can follow along with and build yourself.Project 1 - Simple Companion Chat: Basic chatbot built around a single document.Project 2 - Personal Knowledge Q&A: Ask questions over your own files with grounded answers.Project 3 - Checked Q&A System: Compare AI answers against expected results.Project 4 - Conversational Agent: Multi-turn chat with memory and simple tools.Project 5 - Document Summarizer: Controlled summaries with basic quality checks.Project 6 - Chapter Explorer: Turn text into outlines and short quizzes. These projects mirror modern team workflows and give you something concrete to show in interviews or client work. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 35,43
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Print on Demand.
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 44,92
Envío por EUR 43,05Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Hands-On AI Engineering covers both the construction of LLM systems and the core challenges modern AI teams face daily: performance limits, reliability, evaluation, and cost control.Written by 4 practicing AI engineers. Inside you'll learn how to design, build, and operate LLM systems that r…un efficiently, scale, and perform under pressure. Using local, open-source tools, without expensive cloud credits or black-box APIs.What's included.Training and Fine-Tuning Neural Networks with PyTorch. A set framework on how to effectively train and adapt models using PyTorch.Parameter-efficient fine-tuning with LoRA and QLoRA, and how these techniques make it practical to customize large models on consumer GPUs.Building robust RAG pipelines through smart chunking, hybrid retrieval, intelligent ranking, and faithfulness/grounding mechanisms.Proper evaluation methods including rubrics, LLM-as-a-judge, golden datasets, and regression testing for reliable assessment.Production realities. Key insights into monitoring, guardrails, cost optimization, and reliable deployment of LLM systems.Performance add-ons (last chapter)A companion GitHub repository, carefully sequenced projects you can follow along with and build yourself.Project 1 - Simple Companion Chat: Basic chatbot built around a single document.Project 2 - Personal Knowledge Q&A: Ask questions over your own files with grounded answers.Project 3 - Checked Q&A System: Compare AI answers against expected results.Project 4 - Conversational Agent: Multi-turn chat with memory and simple tools.Project 5 - Document Summarizer: Controlled summaries with basic quality checks.Project 6 - Chapter Explorer: Turn text into outlines and short quizzes. These projects mirror modern team workflows and give you something concrete to show in interviews or client work. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
