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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Practical NLP with Python: Build Real-World NLP Systems Using spaCy, Transformers, and Hugging Face (With Visual diagrams and figure aids.)

Practical NLP with Python: Build Real-World NLP Systems Using spaCy, Transformers, and Hugging Face (With Visual diagrams and figure aids.)

Wayne, Ted

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798269248950
Publisher: Independently Published
Published: Oct 10 2025
Pages: 198
Weight: 0.78
Height: 0.42 Width: 7.00 Depth: 10.00
Language: English

Python stands out as a powerful entry into programming, and this comprehensive guide takes you through the essentials of natural language processing (NLP) with Python, guiding you step by step from foundational concepts to advanced techniques, real-world applications, and the integration of state-of-the-art large language models (LLMs). This edition captures the growing influence of LLMs in NLP, offering hands-on exercises to master tasks like text generation, sentiment analysis, and debugging, all designed for practical application in today's AI landscape. With this approachable yet authoritative resource, you'll build a solid understanding of Python's syntax and semantics tailored for NLP, mastering libraries such as spaCy, Transformers, and Hugging Face while gaining clear insights into each concept through precise, actionable vocabulary. The book walks you through working with text preprocessing, tokenization, and data structures in a logical flow, teaching you to construct and deploy real-world NLP systems with skills in file handling, database integration, and API development. You'll develop expertise in transformer models, fine-tuning approaches, and object-oriented design within NLP frameworks, supported by effective debugging techniques for syntax, runtime, and semantic errors in complex pipelines. The journey extends to advanced areas like intent classification, text summarization, and model optimization, while also exploring how to leverage LLMs through crafting effective prompts, testing models rigorously, and troubleshooting intricate systems.

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