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Item #:
89938768
Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)
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Item #:
89938768
CAD 73
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What Stands Out
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.
Product Details
| 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) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for those starting out in machine learning and seeking to build expertise in transformer models.
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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.
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Complete Beginners
Not suitable for individuals lacking foundational knowledge of programming and machine learning concepts.
Product Description
Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)
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Features & Benefits
- 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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