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AI Engineering
Python

Natural Language Processing with Transformers

Advanced Techniques and Multimodal Applications

From enhancing machine translation systems to developing sophisticated chatbots, this guide provides in-depth insights and practical examples to help you master the most advanced aspects of NLP.

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What You'll Get from This Book

6 chapters spanning over 580 pages

More than 280 explanatories blocks of code

More than 20 practical exercises

3 Quizzes to test your knowledge

6 Practical "Real World" Projects

About thIS book

Enhancing Language Understanding with Advanced Transformers

This book delves into advanced strategies for improving the comprehension capabilities of transformer-based models. Learn how to implement and fine-tune multi-task, transfer, and zero-shot learning techniques to enhance your models' understanding of complex language nuances. The book discusses the latest research in transformer technology, including adaptations and innovations that have shown promise in improving model generalizability and performance across diverse NLP tasks.

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Detailed examples illustrate how these advanced methods are applied in real-world scenarios, such as legal document analysis and biomedical text mining. Each case study explains the technical implementation and evaluates the effectiveness of different approaches, providing readers with a comprehensive understanding of how to apply these advanced techniques in their projects.

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Building Sophisticated NLP Applications

"Natural Language Processing with Transformers: Advanced Techniques and Multimodal Applications" guides you through creating high-impact NLP applications using transformers. This book covers a range of applications, from automated content generation to complex question-answering systems. It provides a step-by-step approach to designing systems that can interpret, generate, and interact using natural language at an advanced level.

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Special attention is given to integrating multimodal data, allowing transformers to process and generate text and information from images and audio. This multidisciplinary approach opens up new possibilities for developing AI systems that more closely mimic human sensory and cognitive abilities.

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The book also addresses the critical aspects of deploying and scaling advanced NLP models. It includes practical advice on optimization techniques, deployment strategies, and performance monitoring to ensure your transformer-based applications are not only powerful but also scalable and efficient. By the end of "Natural Language Processing with Transformers: Advanced Techniques and Multimodal Applications," you will be equipped with the knowledge to develop state-of-the-art NLP applications that can transform industries and enhance user experiences.

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This content aims to effectively capture the depth and breadth of the advanced topics covered in the book, positioning it as an essential resource for professionals in the field. If there are additional elements or specific details you would like to include, please let me know!

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Natural Language Processing (NLP) has rapidly evolved with transformers, the cutting-edge technology powering models like GPT-4, BERT, and T5. Businesses and developers are now leveraging these models for machine translation, chatbots, text summarization, and multimodal applications that integrate text, images, and speech. This book provides a practical and hands-on approach to mastering these state-of-the-art NLP techniques, helping you stay ahead in the AI-driven world.
This book equips you with the knowledge to build and fine-tune powerful NLP models using Hugging Face Transformers, PyTorch, and TensorFlow. Through real-world projects, exercises, and quizzes, you’ll gain expertise in fine-tuning large language models for custom NLP tasks, building scalable APIs for real-world AI applications, optimizing and deploying models for efficiency, and exploring multimodal AI by integrating text with images and speech. By the end of the book, you’ll not only understand transformers conceptually but also practically apply them in production-ready solutions.
Unlike many books that focus only on theory, this book is designed to be practical, hands-on, and up-to-date with the latest NLP advancements. It includes real-world projects like building chatbots, AI-powered search engines, and text summarization models. It covers advanced techniques such as LoRA, Prefix Tuning, and Fine-Tuning GPT models. It also explores multimodal AI by integrating NLP with computer vision and speech processing and provides deployment-ready solutions using ONNX, FastAPI, and cloud platforms for scaling models.
Basic knowledge of Python and Machine Learning is recommended. Experience with deep learning frameworks (PyTorch/TensorFlow) is helpful but not required, as the book provides step-by-step guidance on using Hugging Face and NLP tools. If you’re familiar with data science or software development, this book will help you transition into advanced NLP with transformers effectively.
Access to the Cuantum Technologies VIP customer service, with a dedicated team of developers ready to answer all your questions. A code repository with fully working examples and pre-tested, production-ready code. The Success University e-learning platform, where you can access additional resources and free video content to reinforce your learning. Regular updates and additional materials to stay updated with new advancements in transformers and NLP.

Table of contents

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Chapter 1: Advanced NLP Applications

1.1 Machine Translation

1.2 Text Summarization (Extractive and Abstractive)

1.3 Text Generation with GPT Models

1.4 Practical Exercises

Chapter 1 Summary

Project 1: Machine Translation with MarianMT

Base Project Implementation

Step 1: Setting Up the Environment

Step 2: Loading the MarianMT Model

Step 3: Translating Text

Step 4: Exploring Additional Language Pairs

Project 2: Text Summarization with T5

Base Project Implementation

Step 1: Setting Up the Environment

Step 2: Loading the T5 Model

Step 3: Summarizing Text

Step 4: Adjusting Hyperparameters

Quiz Part I

Multiple Choice Questions

True or False

Short Answer Questions

Answer Key

Chapter 2: Hugging Face and Other NLP Libraries

2.1 Overview of the Hugging Face Ecosystem

2.2 Hugging Face Transformers and Datasets Libraries

2.3 TensorFlow and PyTorch for NLP

2.4 Practical Exercises

Chapter Summary

Chapter 3: Training and Fine-Tuning Transformers

3.1 Data Preprocessing for Transformer Models

3.2 Fine-Tuning Techniques: LoRA and Prefix Tuning

3.3 Evaluation Metrics: BLEU, ROUGE, BERTScore

3.4 Practical Exercises

Chapter Summary

Chapter 4: Deploying and Scaling Transformer Models

4.1 Real-Time Inferencing with ONNX and TensorFlow Lite

4.2 Deploying Models on Cloud Platforms

4.3 Scalable APIs with FastAPI and Hugging Face Spaces

4.4 Practical Exercises

Chapter Summary

Project 3: Sentiment Analysis API with Fine-Tuned Transformer

Steps to Build the Sentiment Analysis API

Step 1: Install Required Libraries

Step 2: Load and Preprocess the Dataset

Step 3: Fine-Tune the Model

Step 4: Build the FastAPI Application

Project 4: Named Entity Recognition (NER) Pipeline with Custom Fine-Tuning

Steps to Build the NER Pipeline

Step 1: Install Required Libraries

Step 2: Load and Preprocess the Dataset

Step 3: Tokenize the Dataset

Step 4: Fine-Tune the Model

Quiz Part II

Multiple-Choice Questions

True or False

Short-Answer Questions

Answer Key

Chapter 5: Innovations and Challenges in Transformers

5.1 Large Language Models: GPT-4, Claude, LLaMA

5.2 Efficient Transformers: Reformer, BigBird, LongFormers

5.3 Ethical AI: Bias and Fairness in Language Models

5.4 Practical Exercises

Chapter Summary

Chapter 6: Multimodal Applications of Transformers

6.1 Vision-Language Models (CLIP, Flamingo)

6.2 Speech Recognition with Whisper

6.3 Multimodal AI: Integration of Text, Image, and Video

6.4 Practical Exercises

Chapter Summary

Project 5: Multimodal Medical Image and Report Analysis with Vision-Language Models

Steps to Build the System

Step 1: Install Required Libraries

Step 2: Load and Preprocess the Data

Step 3: Use CLIP for Image-Text Matching

Step 4: Generate Captions for Medical Images

Project 6: Multimodal Video Analysis and Summarization

Steps to Build the System

Step 1: Install Required Libraries

Step 2: Extract Video Frames

Step 3: Transcribe Audio from Video

Step 4: Perform Video Frame Analysis

Quiz Part III

Multiple-Choice Questions

True or False

Short-Answer Questions

Answer Key

Reviews

What our readers are saying about this book

Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.

Recommended by dozens of people
Review from Amazon

Lesley C.

I really got lucky — this book is like a gold mine of useful information! It is actually 6 books in one, and it takes you through all the essentials of AI with explanations and practical coding exercises. It is the second compilation in a series, so it is actually for readers with intermediate knowledge. It is as complete as a course textbook, which is probably no coincidence since the developer team that produces these books also offers online training. Extremely useful for techies and also if you want to know what AI can do for different business use cases — it evaluates apps, models & frameworks for every need & vertical and quizzes to test your knowledge. It even assesses which models’ architecture are designed to maximize accuracy, transparency , and ethical standards and mitigate bias, and dedicates a section to exercises that teach us how to correct these issues. This is a super exciting resource. I will probably be coming back to it regularly to use it as a handbook.

Review from Amazon

Dave Kuziara

This is a very detailed bool on NLP with transformers (book 2 in the series). It comes with a code repository so you can download any of the examples used. My only grip was that book 1 was very expensive. However, if you want a complete and thorough guild on NLP, then this series comes recommended.

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