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Paperback or Softback. Condizione: New. Computational Intelligence Methods for Bioinformatics and Biostatistics: 7th International Meeting, CIBB 2010, Palermo, Italy, September 16-18, 2010. Book.

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Paperback. Condizione: New. 2011. This book constitutes the thoroughly refereed post-proceedings of the 7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2010, held in Palermo, Italy, in September 2010. The 19 papers, presented together with 2 keynote speeches and 1 tutorial, were carefully reviewed and selected from 24 submissions. The papers are organized in topical sections on sequence analysis, promoter analysis and identification of transcription factor binding sites; methods for the unsupervised analysis, validation and visualization of structures discovered in bio-molecular data -- prediction of secondary and tertiary protein structures; gene expression data analysis; bio-medical text mining and imaging -- methods for diagnosis and prognosis; mathematical modelling and simulation of biological systems; and intelligent clinical decision support systems (i-CDSS).…

Rilegato. Condizione: new. A cura di Perrino G. Bari, 2020; ril., pp. 160, ill. (Fuori Collana). Quante storie e quante pietre del nostro passato abbiamo irrimediabilmente perduto? Per incuria, per incomprensione, per mancanza endemica di denaro e per povertà, per danni fortuiti come terremoti o temporali, per incapacità di conservazione o per indifferenza? È accaduto certo molte volte. Ma non sempre le cose sono perdute. A volte si possono trovare o ritrovare. E così può anche accadere di poter restituire alla propria comunità un pezzo materiale del passato, sia anche una prospettiva, un punto di vista, uno sguardo sulla chiesa. Il presente volume racconta proprio questo: la restituzione dell'antico volto della Chiesa Matrice nel suo prospetto principale, liberato dalle ingombranti fabbriche sovrappostesi nel corso del tempo. Un processo lungo e faticoso, ma portato a termine soprattutto per passione: è questa allora soprattutto una storia d'amore, la storia dell'amore per la chiesa da parte dei suoi fedeli cittadini. Libro.…

Taschenbuch. Condizione: Neu. Neuware.

Condizione: New. In English.

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Paperback. Condizione: Brand New. 2011 edition. 301 pages. 9.00x6.00x0.75 inches. In Stock.

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Paperback. Condizione: Brand New. 192 pages. Italian language. 8.27x5.91x0.47 inches. In Stock.

Paperback. Condizione: New. 2011. This book constitutes the thoroughly refereed post-proceedings of the 7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2010, held in Palermo, Italy, in September 2010. The 19 papers, presented together with 2 keynote speeches and 1 tutorial, were carefully reviewed and selected from 24 submissions. The papers are organized in topical sections on sequence analysis, promoter analysis and identification of transcription factor binding sites; methods for the unsupervised analysis, validation and visualization of structures discovered in bio-molecular data -- prediction of secondary and tertiary protein structures; gene expression data analysis; bio-medical text mining and imaging -- methods for diagnosis and prognosis; mathematical modelling and simulation of biological systems; and intelligent clinical decision support systems (i-CDSS).…

Condizione: Gut. Zustand: Gut | Seiten: 288 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Paperback. Condizione: new. Paperback. Most LLM books show either high-level concepts or finished tools. This book teaches the full engineering path. Build a GPT-Style AI starts from an empty folder and builds a language model project step by step. You will create the environment, tokeniser, dataset pipeline, decoder-only transformer, training loop, checkpoint system, validation reports, mixed precision path, generation module, LoRA workflow, QLoRA workflow, release checklist, and local API. The emphasis is practical understanding. You will see why tokenisation comes before modelling, how next-token training works, how checkpoints protect long runs, why validation loss is not enough, how CUDA AMP uses torch.autocast and GradScaler, why BF16 usually does not need gradient scaling in the same way as FP16, and how CPU and MPS fallbacks should be handled cleanly. The book is honest about scale. A 0.5B model is large enough to teach real engineering pressure. A 7B model changes the hardware conversation. Frontier commercial systems such as GPT-5.5 and Claude require industrial infrastructure, proprietary data pipelines, post-training, safety systems, evaluation programmes, and serving platforms. This book does not pretend otherwise. Instead, it gives you the machinery and judgement to understand how LLM systems are built. You will learn how to: Build a GPT-style transformer directly in PyTorch.Train and inspect a tokeniser.Prepare datasets for next-token prediction.Implement training, validation, checkpointing, and resume.Use CUDA AMP with torch.autocast and GradScaler.Understand BF16, FP16, CPU fallback, and Apple Silicon MPS behaviour.Design a 0.5B model configuration before wasting compute.Reason about scaling from 0.5B to 3B and 7B.Fine tune existing models with LoRA and QLoRA.Evaluate, release, quantise, and serve a model locally.The companion code is available at This is a practical technical book for software engineers, machine learning practitioners, students, founders, and serious builders who want to understand LLMs by constructing the system beneath them. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Italienisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Paperback. Condizione: new. Paperback. Un report ingegneristico dai limiti dell'addestramento locale di IA.I grandi modelli linguistici, dall'esterno, possono apparire magici.Questo libro rimuove quell'illusione.Ingegnerizzare un piccolo modello linguistico di IA e un resoconto pratico e tecnicamente fondato su cosa significa progettare, addestrare, valutare e mettere in produzione un moderno modello linguistico sotto vincoli reali.Scritto dalla prospettiva di un singolo ingegnere che lavora su hardware consumer, il libro esplora l'intero sistema dietro un modello in stile GPT - dalla tokenizzazione e architettura alla distribuzione dei dati, alle dinamiche di ottimizzazione e ai contratti di deploy.Nel libro esplorerai: - i limiti fisici di VRAM, throughput e lunghi cicli di addestramento - la tokenizzazione come interfaccia strutturale, non semplice preprocessing - come la distribuzione dei dati plasmi il comportamento piu dell'architettura - LoRA, QLoRA e l'addestramento sotto severi vincoli hardware - pipeline di valutazione, gate di regressione e stabilita comportamentale - la differenza tra fluidita, affidabilita e vero system design Questo non e un tutorial ne un trattato teorico. E un report dal confine dell'ingegneria, dove le idee incontrano hardware, dati e tempo. 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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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PF. Condizione: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Condizione: new. Paperback. Most LLM books show either high-level concepts or finished tools. This book teaches the full engineering path. Build a GPT-Style AI starts from an empty folder and builds a language model project step by step. You will create the environment, tokeniser, dataset pipeline, decoder-only transformer, training loop, checkpoint system, validation reports, mixed precision path, generation module, LoRA workflow, QLoRA workflow, release checklist, and local API. The emphasis is practical understanding. You will see why tokenisation comes before modelling, how next-token training works, how checkpoints protect long runs, why validation loss is not enough, how CUDA AMP uses torch.autocast and GradScaler, why BF16 usually does not need gradient scaling in the same way as FP16, and how CPU and MPS fallbacks should be handled cleanly. The book is honest about scale. A 0.5B model is large enough to teach real engineering pressure. A 7B model changes the hardware conversation. Frontier commercial systems such as GPT-5.5 and Claude require industrial infrastructure, proprietary data pipelines, post-training, safety systems, evaluation programmes, and serving platforms. This book does not pretend otherwise. Instead, it gives you the machinery and judgement to understand how LLM systems are built. You will learn how to: Build a GPT-style transformer directly in PyTorch.Train and inspect a tokeniser.Prepare datasets for next-token prediction.Implement training, validation, checkpointing, and resume.Use CUDA AMP with torch.autocast and GradScaler.Understand BF16, FP16, CPU fallback, and Apple Silicon MPS behaviour.Design a 0.5B model configuration before wasting compute.Reason about scaling from 0.5B to 3B and 7B.Fine tune existing models with LoRA and QLoRA.Evaluate, release, quantise, and serve a model locally.The companion code is available at This is a practical technical book for software engineers, machine learning practitioners, students, founders, and serious builders who want to understand LLMs by constructing the system beneath them. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fast-track conference proceedingsState-of-the-art researchUp-to-date resultsFast-track conference proceedingsState-of-the-art researchUp-to-date resultsThis book constitutes the thoroughly refereed post-pr.…

Paperback. Condizione: new. Paperback. Un report ingegneristico dai limiti dell'addestramento locale di IA.I grandi modelli linguistici, dall'esterno, possono apparire magici.Questo libro rimuove quell'illusione.Ingegnerizzare un piccolo modello linguistico di IA e un resoconto pratico e tecnicamente fondato su cosa significa progettare, addestrare, valutare e mettere in produzione un moderno modello linguistico sotto vincoli reali.Scritto dalla prospettiva di un singolo ingegnere che lavora su hardware consumer, il libro esplora l'intero sistema dietro un modello in stile GPT - dalla tokenizzazione e architettura alla distribuzione dei dati, alle dinamiche di ottimizzazione e ai contratti di deploy.Nel libro esplorerai: - i limiti fisici di VRAM, throughput e lunghi cicli di addestramento - la tokenizzazione come interfaccia strutturale, non semplice preprocessing - come la distribuzione dei dati plasmi il comportamento piu dell'architettura - LoRA, QLoRA e l'addestramento sotto severi vincoli hardware - pipeline di valutazione, gate di regressione e stabilita comportamentale - la differenza tra fluidita, affidabilita e vero system design Questo non e un tutorial ne un trattato teorico. E un report dal confine dell'ingegneria, dove le idee incontrano hardware, dati e tempo. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…