Machine learning writers (8 risultati)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 33,03
EUR 2,27 spedizioneSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New.
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 36,76
EUR 2,27 spedizioneSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: As New. Unread book in perfect condition.
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 43,92
EUR 17,45 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Condizione: As New. Unread book in perfect condition.
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 44,91
EUR 17,45 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Condizione: New.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 45,54
EUR 30,50 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: 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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- Print on Demand
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 35,36
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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 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.
- Brossura
- Print on Demand
Da: California Books, Miami, FL, U.S.A.California Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 35,43
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Print on Demand.
- Brossura
- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 44,92
EUR 43,05 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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 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.
