9798868822964 - building large language models from scratch: design, train, and deploy llms with pytorch de grigorov, dilyan (17 resultados)
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Paperback. Condición: new. Paperback. This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges…core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Starting from the essentials, youll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. Youll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU levelan essential skill for scaling real-world LLMs. Youll also gain mastery over the phases of training that define todays leading models:Pretraining - Building general linguistic and semantic understanding.Midtraining - Expanding domain-specific capabilities and adaptability.Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.By the end of this book, youll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.What Youll LearnHow to configure and optimize your development environment using PyTorchThe mechanics of tokenization, embeddings, normalization, and attention mechanisms.How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.How to integrate custom CUDA kernels to accelerate transformer computations.The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.Who this book is for:Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Taschenbuch. Condición: Neu. Neuware -This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs) from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges… core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Starting from the essentials, you ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level an essential skill for scaling real-world LLMs. You ll also gain mastery over the phases of training that define today s leading models:Pretraining - Building general linguistic and semantic understanding.Midtraining - Expanding domain-specific capabilities and adaptability.Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.By the end of this book, you ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.What You ll LearnHow to configure and optimize your development environment using PyTorchThe mechanics of tokenization, embeddings, normalization, and attention mechanisms.How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.How to integrate custom CUDA kernels to accelerate transformer computations.The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks. 530 pp. Englisch.
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Taschenbuch. Condición: Neu. Neuware -This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges…core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.
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Taschenbuch. Condición: Neu. Building Large Language Models from Scratch | Design, Train, and Deploy LLMs with PyTorch | Dilyan Grigorov | Taschenbuch | xxv | Englisch | 2026 | Apress | EAN 9798868822964 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[…dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condición: Neu. Neuware -This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges…core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 530 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. Neuware - This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs) from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridge…s core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Starting from the essentials, you ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level an essential skill for scaling real-world LLMs. You ll also gain mastery over the phases of training that define today s leading models:Pretraining - Building general linguistic and semantic understanding.Midtraining - Expanding domain-specific capabilities and adaptability.Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.By the end of this book, you ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.What You ll LearnHow to configure and optimize your development environment using PyTorchThe mechanics of tokenization, embeddings, normalization, and attention mechanisms.How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.How to integrate custom CUDA kernels to accelerate transformer computations.The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.
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Librería: Books-by-Floh, Paderborn, AlemaniaBooks-by-Floh
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Taschenbuch. Condición: Neu. Neuware -This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges…core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Starting from the essentials, you'll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You'll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU levelan essential skill for scaling real-world LLMs. You'll also gain mastery over the phases of training that define today's leading models:- Pretraining - Building general linguistic and semantic understanding.- Midtraining - Expanding domain-specific capabilities and adaptability.- Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.- Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.By the end of this book, you'll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.What You'll Learn- How to configure and optimize your development environment using PyTorch- The mechanics of tokenization, embeddings, normalization, and attention mechanisms.- How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.- How to integrate custom CUDA kernels to accelerate transformer computations.- The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.- Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.- How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.Who this book is for:Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch. 556 pp. Englisch.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs) from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for d…evelopers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Starting from the essentials, you ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level an essential skill for scaling real-world LLMs. You ll also gain mastery over the phases of training that define today s leading models:Pretraining - Building general linguistic and semantic understanding.Midtraining - Expanding domain-specific capabilities and adaptability.Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.By the end of this book, you ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.What You ll LearnHow to configure and optimize your development environment using PyTorchThe mechanics of tokenization, embeddings, normalization, and attention mechanisms.How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.How to integrate custom CUDA kernels to accelerate transformer computations.The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks. 530 pp. Englisch.
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Librería: moluna, Greven, Alemaniamoluna
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