Transformer Models: 33 Comprehensively Commented Python Implementations of Transformer Models (Stochastic Sorcerers) - Softcover
Inhaltsangabe
A Powerful Academic Resource on Transformer-Based Models
Immerse yourself in cutting-edge Transformer architectures, where advanced research and practical implementation converge. This comprehensive resource uses full Python code to guide you from foundational concepts to sophisticated real-world applications. Whether you're a researcher seeking rigorous theoretical underpinnings or a professional aiming for state-of-the-art performance across NLP, computer vision, and multi-modal tasks, this text delivers clear explanations, hands-on tutorials, and innovative best practices.
Highlights of Featured Algorithms- Text Classification with Pre-Trained Models
Delve into advanced fine-tuning techniques that boost accuracy across sentiment analysis and topic allocation tasks.
- Aspect-Based Sentiment Analysis
Extract nuanced opinions on specific product or service attributes with specialized attention mechanisms.
- Vision Transformers for Image Classification
Discover how sequence-based patch embeddings enable remarkable object recognition accuracy on complex datasets.
- Named Entity Recognition
Implement robust token-level labelers strengthened by deep contextual embeddings, critical for biomedical or financial text.
- Time-Series Forecasting
Uncover the long-term temporal dependencies in stock data or IoT sensor readings using multi-head self-attention.
- Graph Transformers for Node Classification
Capture intricate relationships in social networks or molecular structures with specialized structural embeddings and graph-based attention.
- Zero-Shot Classification
Classify unseen data on-the-fly by leveraging prompt-based approaches and semantic embeddings learned from extensive pre-training.
Packed with step-by-step instructions, well-documented code, and time-tested optimization tips, this resource equips you to push Transformer capabilities to their limits—across both emerging and established domains.
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Transformer Models (Paperback)
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Paperback. Zustand: new. Paperback. A Powerful Academic Resource on Transformer-Based Models Immerse yourself in cutting-edge Transformer architectures, where advanced research and practical implementation converge. This comprehensive resource uses full Python code to guide you from foundational concepts to sophisticated real-world applications. Whether you're a researcher seeking rigorous theoretical underpinnings or a professional aiming for state-of-the-art performance across NLP, computer vision, and multi-modal tasks, this text delivers clear explanations, hands-on tutorials, and innovative best practices.Highlights of Featured AlgorithmsText Classification with Pre-Trained ModelsDelve into advanced fine-tuning techniques that boost accuracy across sentiment analysis and topic allocation tasks. Aspect-Based Sentiment AnalysisExtract nuanced opinions on specific product or service attributes with specialized attention mechanisms. Vision Transformers for Image ClassificationDiscover how sequence-based patch embeddings enable remarkable object recognition accuracy on complex datasets. Named Entity RecognitionImplement robust token-level labelers strengthened by deep contextual embeddings, critical for biomedical or financial text. Time-Series ForecastingUncover the long-term temporal dependencies in stock data or IoT sensor readings using multi-head self-attention. Graph Transformers for Node ClassificationCapture intricate relationships in social networks or molecular structures with specialized structural embeddings and graph-based attention. Zero-Shot ClassificationClassify unseen data on-the-fly by leveraging prompt-based approaches and semantic embeddings learned from extensive pre-training. Packed with step-by-step instructions, well-documented code, and time-tested optimization tips, this resource equips you to push Transformer capabilities to their limits-across both emerging and established domains. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798307414415
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Transformer Models
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Transformer Models: 33 Comprehensively Commented Python Implementations of Transformer Models (Stochastic Sorcerers)
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Paperback. Zustand: new. Paperback. A Powerful Academic Resource on Transformer-Based Models Immerse yourself in cutting-edge Transformer architectures, where advanced research and practical implementation converge. This comprehensive resource uses full Python code to guide you from foundational concepts to sophisticated real-world applications. Whether you're a researcher seeking rigorous theoretical underpinnings or a professional aiming for state-of-the-art performance across NLP, computer vision, and multi-modal tasks, this text delivers clear explanations, hands-on tutorials, and innovative best practices.Highlights of Featured AlgorithmsText Classification with Pre-Trained ModelsDelve into advanced fine-tuning techniques that boost accuracy across sentiment analysis and topic allocation tasks. Aspect-Based Sentiment AnalysisExtract nuanced opinions on specific product or service attributes with specialized attention mechanisms. Vision Transformers for Image ClassificationDiscover how sequence-based patch embeddings enable remarkable object recognition accuracy on complex datasets. Named Entity RecognitionImplement robust token-level labelers strengthened by deep contextual embeddings, critical for biomedical or financial text. Time-Series ForecastingUncover the long-term temporal dependencies in stock data or IoT sensor readings using multi-head self-attention. Graph Transformers for Node ClassificationCapture intricate relationships in social networks or molecular structures with specialized structural embeddings and graph-based attention. Zero-Shot ClassificationClassify unseen data on-the-fly by leveraging prompt-based approaches and semantic embeddings learned from extensive pre-training. Packed with step-by-step instructions, well-documented code, and time-tested optimization tips, this resource equips you to push Transformer capabilities to their limits-across both emerging and established domains. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798307414415
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Flux, J: Transformer Models
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