9785161063675 - methods to improve ai code generation de rashid, mohd (9 resultados)
- Tapa blanda
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 14,34
Envío por EUR 3,88Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
- Tapa blanda
Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 24,53
Envío por EUR 3,48Se envía dentro de Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New.
- Tapa blanda
- Impresión bajo demanda
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 15,13
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. In recent years, transformer-based AI language models have gained prominence due to their powerful capabilities in a variety of tasks including generation of images, video, text and code. Large Language Models (LLMs) exist with parameters counts of a trillion parameters and greater. Such mod…els are proprietary and unavailable for organizations to deploy privately. Even if such deployments were possible, the tremendous resource requirements of LLMs preclude their deployment on infrastructure smaller than enterprise and hyper-scale data centers. Small Language Models (SLMs), with far lower parameter counts of billions or fewer are a viable alternative for use on small servers and edge devices including PCs. While SLMs possess similar generative capabilities as LLMs, the reduction in model size is correlated with a decrease in accuracy when evaluated across a broad range of generative applications, including code generation in multiple languages.To mitigate this shortcoming, an SLM may be fine-tuned with a curated code dataset consisting of code examples in a target programming language. This praxis presents results illustrating how two fine-tuned SLMs variants have been created that improve average accuracy in C++ code generation by more than 9%, and Rust code generation by more than 14%. This praxis defines the problem space of code generation with SLMs and provides an in-depth review of the methods used to create the ensemble model. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 20,64
Envío por EUR 7,67Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
- Tapa blanda
- Impresión bajo demanda
Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 21,09
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
- Tapa blanda
- Impresión bajo demanda
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 16,88
Envío por EUR 32,26Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. In recent years, transformer-based AI language models have gained prominence due to their powerful capabilities in a variety of tasks including generation of images, video, text and code. Large Language Models (LLMs) exist with parameters counts of a trillion parameters and greater. Such mod…els are proprietary and unavailable for organizations to deploy privately. Even if such deployments were possible, the tremendous resource requirements of LLMs preclude their deployment on infrastructure smaller than enterprise and hyper-scale data centers. Small Language Models (SLMs), with far lower parameter counts of billions or fewer are a viable alternative for use on small servers and edge devices including PCs. While SLMs possess similar generative capabilities as LLMs, the reduction in model size is correlated with a decrease in accuracy when evaluated across a broad range of generative applications, including code generation in multiple languages.To mitigate this shortcoming, an SLM may be fine-tuned with a curated code dataset consisting of code examples in a target programming language. This praxis presents results illustrating how two fine-tuned SLMs variants have been created that improve average accuracy in C++ code generation by more than 9%, and Rust code generation by more than 14%. This praxis defines the problem space of code generation with SLMs and provides an in-depth review of the methods used to create the ensemble model. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 18,23
Envío por EUR 43,68Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. In recent years, transformer-based AI language models have gained prominence due to their powerful capabilities in a variety of tasks including generation of images, video, text and code. Large Language Models (LLMs) exist with parameters counts of a trillion parameters and greater. Such mod…els are proprietary and unavailable for organizations to deploy privately. Even if such deployments were possible, the tremendous resource requirements of LLMs preclude their deployment on infrastructure smaller than enterprise and hyper-scale data centers. Small Language Models (SLMs), with far lower parameter counts of billions or fewer are a viable alternative for use on small servers and edge devices including PCs. While SLMs possess similar generative capabilities as LLMs, the reduction in model size is correlated with a decrease in accuracy when evaluated across a broad range of generative applications, including code generation in multiple languages.To mitigate this shortcoming, an SLM may be fine-tuned with a curated code dataset consisting of code examples in a target programming language. This praxis presents results illustrating how two fine-tuned SLMs variants have been created that improve average accuracy in C++ code generation by more than 9%, and Rust code generation by more than 14%. This praxis defines the problem space of code generation with SLMs and provides an in-depth review of the methods used to create the ensemble model. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 15,63
Envío por EUR 60,73Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In recent years, transformer-based AI language models have gained prominence due to their powerful capabilities in a variety of tasks including generation of images, video, text and code. Large Language Models (LLMs) exist with parameter…s counts of a trillion parameters and greater. Such models are proprietary and unavailable for organizations to deploy privately. Even if such deployments were possible, the tremendous resource requirements of LLMs preclude their deployment on infrastructure smaller than enterprise and hyper-scale data centers. Small Language Models (SLMs), with far lower parameter counts of billions or fewer are a viable alternative for use on small servers and edge devices including PCs. While SLMs possess similar generative capabilities as LLMs, the reduction in model size is correlated with a decrease in accuracy when evaluated across a broad range of generative applications, including code generation in multiple languages.To mitigate this shortcoming, an SLM may be fine-tuned with a curated code dataset consisting of code examples in a target programming language. This praxis presents results illustrating how two fine-tuned SLMs variants have been created that improve average accuracy in C++ code generation by more than 9%, and Rust code generation by more than 14%.
- Tapa blanda
- Impresión bajo demanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 15,55
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Methods to Improve AI Code Generation | Mohd Rashid | Taschenbuch | Englisch | 2026 | RASHID PUBLICATIONS | EAN 9785161063675 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
