Writers machine learning (8 résultats)
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Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
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EUR 33,03
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Etat : New.
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Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 36,76
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Etat : As New. Unread book in perfect condition.
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 43,92
EUR 17,45 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : As New. Unread book in perfect condition.
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 44,91
EUR 17,45 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New.
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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EUR 45,54
EUR 30,50 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : 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.
- Couverture souple
- impression à la demande
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail
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EUR 35,36
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : 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 run e…fficiently, 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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- impression à la demande
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 35,43
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New. Print on Demand.
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- impression à la demande
Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 44,92
EUR 43,05 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : 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 run e…fficiently, 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.
