Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)
Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)
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Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)
Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)
Your key to transformer based NLP, vision, speech, and multimodalities.
Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)
Numéro d'article: 89938768

Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)

Numéro d'article: 89938768

€ 47

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Ce qui se démarque

Comprehensive Coverage
Covers NLP, computer vision, and speech processing, offering a holistic understanding of transformer models' applications in multiple domains, making it suitable for diverse interests.
Latest Framework Features
Utilizes PyTorch 2.0, ensuring readers learn cutting-edge techniques and tools, keeping them ahead in the rapidly evolving field of machine learning.
Hands-on Examples
Provides practical examples using Hugging Face, enabling readers to apply concepts immediately, enhancing learning through real-world projects and increasing engagement.

Détails du produit

Shop Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition) online at a best price in Mayotte. 9355517491
  • Your key to transformer based NLP, vision, speech, and multimodalitiesKey Features● Transformer architecture for different modalities and multimodalities.● Practical guidelines to build and fine-tune transformer models.● Comprehensive code samples with detailed documentation.DescriptionThis book covers transformer architecture for various applications including NLP, computer vision, speech processing, and predictive modeling with tabular data. It is a valuable resource for anyone looking to harness the power of transformer architecture in their machine learning projects.The book provides a step-by-step guide to building transformer models from scratch and fine-tuning pre-trained open-source models. It explores foundational model architecture, including GPT, VIT, Whisper, TabTransformer, Stable Diffusion, and the core principles for solving various problems with transformers. The book also covers transfer learning, model training, and fine-tuning, and discusses how to utilize recent models from Hugging Face. Additionally, the book explores advanced topics such as model benchmarking, multimodal learning, reinforcement learning, and deploying and serving transformer models.In conclusion, this book offers a comprehensive and thorough guide to transformer models and their various applications.What you will learn● Understand the core architecture of various foundational models, including single and multimodalities.● Step-by-step approach to developing transformer-based Machine Learning models.● Utilize various open-source models to solve your business problems.● Train and fine-tune various open-source models using PyTorch 2.0 and the Hugging Face ecosystem.● Deploy and serve transformer models.● Best practices and guidelines for building transformer-based models.Who this book is forThis book caters to data scientists, Machine Learning engineers, developers, and software architects interested in the world of generative AI.Table of Contents1. Transformer Architecture2. Hugging Face Ecosystem3. Transformer Model in PyTorch4. Transfer Learning with PyTorch and Hugging Face5. Large Language Models: BERT, GPT-3, and BART6. NLP Tasks with Transformers7. CV Model Anatomy: ViT, DETR, and DeiT8. Computer Vision Tasks with Transformers9. Speech Processing Model Anatomy: Whisper, SpeechT5, and Wav2Vec10. Speech Tasks with Transformers11. Transformer Architecture for Tabular Data Processing12. Transformers for Tabular Data Regression and Classification13. Multimodal Transformers, Architectures and Applications14. Explore Reinforcement Learning for Transformer15. Model Export, Serving, and Deployment16. Transformer Model Interpretability, and Experimental Visualization17. PyTorch Models: Best Practices and Debugging
Publisher BPB Publications
Publication date March 8, 2024
Language English
Print length 310 pages
ISBN-10 9355517491
ISBN-13 978-9355517494
Item Weight 1.12 pounds (510 grams)
Dimensions 7.5 x 0.7 x 9.25 inches (19.1 x 1.8 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Aspiring Data Scientists

    Ideal for those starting out in machine learning and seeking to build expertise in transformer models.

  • Machine Learning Professionals

    Beneficial for practitioners looking to enhance their skills in NLP, CV, and speech processing using PyTorch.

  • Educators and Trainers

    Useful for instructors teaching modern AI topics, providing valuable examples and practical applications of transformers.

Not Suitable For
  • Complete Beginners

    Not suitable for individuals lacking foundational knowledge of programming and machine learning concepts.

DESCRIPTION DU PRODUIT

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Questions et réponses des clients

  • question: What is the focus of the book 'Building Transformer Models With PyTorch 2.0'?

    répondre: The book primarily focuses on how to build and implement transformer models using PyTorch 2.0, covering various domains such as natural language processing (NLP), computer vision, and speech processing. It delves into the architecture of transformer models and highlights their applications in creating state-of-the-art solutions for these fields. Readers will benefit from practical examples and detailed explanations that facilitate a deep understanding of both theoretical concepts and hands-on implementation.
  • question: Who is the target audience for this book?

    répondre: This book is designed for a wide range of audiences, including students, researchers, data scientists, and software developers who are interested in learning about transformer models and their applications. Prior experience with PyTorch and basic machine learning concepts is beneficial but not strictly required. By targeting readers with varying expertise, the book bridges the gap between foundational knowledge and advanced applications, making it accessible for anyone wanting to enhance their skills in AI-driven fields.
  • question: Does the book cover practical implementations?

    répondre: Yes, 'Building Transformer Models With PyTorch 2.0' emphasizes practical implementations along with theoretical concepts. Each chapter presents real-world examples and code snippets, which allow readers to apply what they've learned through hands-on exercises. This approach not only solidifies understanding but also prepares readers to deploy models in practical applications. By following along with the book, users can build their own projects involving NLP, computer vision, and speech processing.
  • question: What can I learn about natural language processing in this book?

    répondre: The book contains comprehensive sections dedicated to natural language processing (NLP), where readers will learn how to construct transformer models that excel in tasks like text classification, sentiment analysis, and language translation. It provides insights into the nuances of language representation and preprocessing techniques, enabling readers to fine-tune models for specific NLP tasks. Each topic is supported by clear coding examples, illustrating practical applications that one might encounter in real-world scenarios.
  • question: Are there any prerequisites to reading this book?

    répondre: While the book is structured to be accessible to a variety of readers, a basic understanding of Python programming, machine learning concepts, and familiarity with PyTorch will enhance the learning experience. For beginners, supplementary resources may be helpful to establish foundational knowledge. This ensures a smoother journey through the book’s detailed explanations and coding examples, allowing readers to effectively engage with the material and build their proficiency in transformer models.
  • question: What types of projects can I build using the concepts from this book?

    répondre: Readers can leverage the methodologies discussed in the book to develop a variety of projects across different domains. For instance, you might create a chatbot utilizing NLP capabilities, implement real-time image classification systems for computer vision, or construct a speech recognition application. The concepts are versatile, and the knowledge gained can be applied to innovative solutions in fields like healthcare, finance, and entertainment, providing a strong foundation for project-based learning.
  • question: Is there a focus on Hugging Face libraries in the book?

    répondre: Yes, the book includes a significant focus on Hugging Face libraries, which are essential tools for working with transformer models. Readers are guided through the usage of these libraries for simplifying the development and deployment processes. This integration allows users to take advantage of pre-trained models and efficient tokenizers, highlighting best practices for model training and optimization for real-world applications in NLP, computer vision, and speech processing.
  • question: How does this book differentiate itself from others on the same topic?

    répondre: This book sets itself apart by providing a blend of both theoretical insights and practical implementation examples, making the learning experience robust and applicable. Its focus on recent advancements in PyTorch 2.0, alongside the inclusion of Hugging Face libraries, ensures that readers are working with up-to-date methodologies. Additionally, the structured approach leads from foundational concepts to advanced applications, making it suitable for both newcomers and experienced practitioners looking to deepen their expertise.
  • question: What skills will I improve upon by reading this book?

    répondre: By engaging with 'Building Transformer Models With PyTorch 2.0', readers will significantly enhance their understanding of deep learning, specifically in the context of transformer architectures. Skills in programming, model training, and the application of AI techniques in various fields will be honed. Furthermore, the ability to troubleshoot and optimize models for performance will greatly benefit readers, making them well-prepared for tackling complex AI problems in their professional or academic careers.
  • question: Where can I buy 'Building Transformer Models With PyTorch 2.0 NLP, Computer Vision, And Speech Processing With PyTorch And Hugging Face English Edition'?

    répondre: You can buy 'Building Transformer Models With PyTorch 2.0' from Ubuy in Mayotte. Ubuy offers a platform where you can easily find this book along with other related resources. By purchasing through Ubuy, you gain access to a wide selection of books and materials that are essential in the realm of AI and machine learning.

Natural Language Processing Editorial Review

In the world of machine learning and data science, understanding transformer models is becoming increasingly crucial. "Building Transformer Models with PyTorch 2.0" has been well-received by readers looking to dive into the practical applications of these advanced concepts. Overall, customers appreciate the book's structured approach and clear explanations, which make complex topics more accessible, especially for those with limited prior knowledge. Many reviewers highlighted the book's effectiveness in teaching the fundamentals of transformer models alongside practical experience. The step-by-step instructions and readily available code examples significantly enhance the learning process, allowing readers to engage actively with the material. This hands-on approach, coupled with the logical sequence of content, encourages a deeper understanding of the architecture and various applications, such as in natural language processing, computer vision, and speech processing. The inclusion of quizzes also adds an interactive element, enabling learners to assess their understanding as they progress. Notably, some customers expressed that the initial chapters provide a solid foundation in transformer architecture, making it easier to tackle more advanced topics. The availability of visual aids and links to supplementary resources, such as Google Colab files, further enrich the learning experience. Meanwhile, a few isolated mentions of issues regarding the book's content being misaligned with the cover suggest that while there are occasional discrepancies, they do not significantly detract from the overall value of the book. In summary, "Building Transformer Models with PyTorch 2.0" serves as an excellent introduction to the field of machine learning, combining theory and practice effectively, making it a top choice for both beginners and those looking to solidify their knowledge in a rapidly evolving domain. **

Avis et évaluations clients

22 évaluations des clients
  • 5 étoile
    55%
  • 4 étoile
    9%
  • 3 étoile
    6%
  • 2 étoile
    21%
  • 1 étoile
    9%

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Avantages

  • Structured and logical presentation of complex topics
  • Step-by-step practical examples
  • Readily available code examples on Google Colab
  • Interactive quizzes for knowledge assessment
  • Helpful visual aids for understanding architecture

Les inconvénients

  • Occasional content discrepancies noted by a few readers

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Caractéristiques et avantages

  • Explore advanced machine learning topics like model debugging and reinforcement learning.
  • Dual-chapter approach connects theoretical knowledge with practical skills across major domains.
  • Hands-on activities engage readers and solidify learning.
  • Includes a dedicated chapter on utilizing the Hugging Face ecosystem for model training and deployment.
  • Comprehensive insights into large language models such as BERT and GPT-3.
  • Step-by-step guidance on building and fine-tuning transformer models for varied applications.

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