دانلود کتاب پردازش زبان طبیعی با ترانسفورماتورها و پایتون، راهکارهای عملی هوش مصنوعی

Natural Language Processing (NLP) has always been one of the most fascinating areas of artificial intelligence. From enabling search engines to understand queries to powering chatbots, virtual assistants, and even machine translation, NLP has transformed the way we interact with technology. But in recent years, one breakthrough has taken NLP to unprecedented heights: transformer models. If you’ve ever marveled at how ChatGPT, Google’s BERT, or Meta’s LLaMA generate human-like text, summarize articles, or answer questions with surprising accuracy, you’ve witnessed the power of transformers. These models are the backbone of modern NLP, offering state-of-the-art performance in everything from text classification to summarization and even multimodal AI, where language meets images and speech. But despite their power, getting started with transformers can feel overwhelming. The concepts—self-attention, tokenization, fine-tuning— can seem complex. The ever-growing list of frameworks and tools— Hugging Face Transformers, PyTorch, TensorFlow—can be intimidating. And with the rapid pace of AI advancements, it’s easy to feel lost. I wanted to create a practical, hands-on guide that takes you from understanding the fundamentals of NLP and transformers to implementing real-world solutions using Python. Whether you’re a machine learning engineer, data scientist, software developer, or AI enthusiast, this book will provide you with the tools, knowledge, and confidence to work with cutting-edge NLP models. Throughout this book, I’ve prioritized clarity, real-world examples, and hands-on tutorials. Instead of drowning you in theory, you’ll find step-bystep instructions, Python code, and best practices to help you build, finetune, and deploy transformer-based NLP applications.