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The book is all about the basics of NLP, generative AI, and their specific component LLM. In this book, we have provided conceptual knowledge about different terminologies and concepts of NLP and NLG with practical hands-on. This comprehensive book offers a deep dive into the world of NLP and LLMs. Starting with the fundamentals of Python programming and code editors, the book gradually introduces NLP concepts, including text preprocessing, word embeddings, and transformer architectures. You will explore the architecture and capabilities of popular models like GPT-3 and BERT. The book also covers practical aspects of LLM usage for RAG applications using frameworks like LangChain and Hugging Face and deploying them in real world applications. With a focus on both theoretical knowledge and hands-on experience, this book is ideal for anyone looking to master the art of NLP and LLMs. The book also contains AWS Cloud deployment, which will help readers step into the world of cloud computing. As the book contains both theoretical and practical approaches, it will help the readers to gain confidence in the deployment of LLMs for any use cases, as well as get acquainted with the required generative AI knowledge to crack the interviews. Key Features ● Covers Python basics, NLP concepts, and terminologies, including LLM and RAG concepts. ● Provides exposure to LangChain, Hugging Face ecosystem, and chatbot creation using custom data. ● Guides on integrating chatbots with real-time applications and deploying them on AWS Cloud. What you will learn ● Basics of Python, which contains Python concepts, installation, and code editors. ● Foundation of NLP and generative AI concepts and different terminologies being used in NLP and generative AI domain. ● LLMs and their importance in the cutting edge of AI. ● Creating chatbots using custom data using open source LLMs without spending a single penny. ● Integration of chatbots with real-world applications like Telegram. Who this book is for This book is ideal for beginners and freshers entering the AI or ML field, as well as those at an intermediate level looking to deepen their understanding of generative AI, LLMs, and cloud deployment. Cover Title Page Copyright Page Dedication Page About the Authors About the Reviewer Acknowledgements Preface Table of Contents 1. Introduction to Python and Code Editors Introduction Structure Objectives Introduction to Python Introduction to code editors Conclusion References Further reading 2. Installation of Python, Required Packages, and Code Editors Introduction Structure Objectives General instructions Installation of Python on Windows Installation of Python on Linux Installation of Python on MacOS Using Docker for Python Installation of IDE Installation of PyCharm Installation of required packages Virtual environment virtualenv pipenv Folder structure Creating a virtual environment PEP 8 standards Following PEP 8 in PyCharm Object-Oriented Programming concepts in Python Classes in Python Functions in Python For loop in Python While loop in Python If-else in Python Conclusion 3. Ways to Run Python Scripts Introduction Structure Objectives Setting up the project Running Python scripts from PyCharm Running Python Scripts from Terminal Running Python scripts from Jupyter Lab and Notebook Running Python Scripts from Docker Conclusion 4. Introduction of NLP and its concepts Introduction Structure Objectives Natural Language Processing overview Key concepts Corpus N-grams Tokenization Difference in tokens and n-grams Stop words removal Stemming Lemmatization Lowercasing Part-of-speech tagging Named Entity Recognition Bag of words Word embeddings Topic modeling Sentiment analysis Large language models Transfer learning Text classification Prompt engineering Hallucination Syntactic relationship Semantic relationship Conclusion 5. Introduction to Large Language Models Introduction Structure Objectives History LLM use cases LLM terminologies Neural networks Transformers Pre-built transformers Bidirectional Encoder Representations from Transformers Generative Pre-trained Transformer Text-to-text transfer transformer DistilBERT XLNet RoBERTa Conclusion Further readings References 6. Introduction to LangChain, Usage and Importance Introduction Structure Objectives LangChain overview Installation and setup Usages Opensource LLM models usage Data loaders Opensource text embedding models usage Vector stores Model comparison Evaluation Types of evaluation Conclusion Points to remember References 7. Introduction to Hugging Face, its Usage and Importance Introduction Structure Objectives Exploring the Hugging Face platform Installation and setup Datasets Usage of opensource LLMs Generating vector embeddings Evaluation Transfer learning with Hugging Face API Real-world use cases of Hugging Face Conclusion References 8. Creating Chatbots using Custom Data with Langchain and Hugging Face Hub Introduction Structure Objectives Setup Overview Steps to create RAG based chatbot with custom data Dolly-V2-3B details Data loaders by LangChain Vector stores by LangChain Conclusion References 9. Hyperparameter Tuning and Fine Tuning Pre-Trained Models Introduction Structure Objectives Hyperparameters of an LLM Hyperparameters at inferencing or at text generation Fine-tuning of an LLM Data preparation for finetuning an LLM Performance improvement Conclusion References 10. Integrating LLMs into Real-World Applications: Case Studies Introduction Structure Objectives Case studies Use case with Telegram Setup Conclusion References 11. Deploying LLMs in Cloud Environments for Scalability Introduction Structure Objectives Amazon Web Services Step 1: Creating an Amazon SageMaker Notebook Instance Step 2: Create folders in SageMaker to store data Step 3: Create vector embeddings Step 4: Auto scaling Google Cloud Platform Conclusion References 12. Future Directions: Advances in LLMs and Beyond Introduction Structure Objectives Generative AI market growth Reasoning Emergence of multimodal models Small domain-specific models Multi agent framework Quantization and Parameter-Efficient Fine Tuning Vector databases Guardrails Model evaluation frameworks Ethical and bias mitigation Safety and security Conclusion References Appendix A: Useful Tips for Efficient LLM Experimentation Structure Objectives Understanding the challenges of LLM experimentation Preparing data for LLM experimentation Optimizing model architecture and hyperparameters Efficient training strategies for LLMs Evaluating and interpreting experimental results Fine-tuning for specific applications Scaling up: Distributed training and parallel processing Deployment considerations for LLMs Conclusion References Appendix B: Resources and References Introduction Books and articles Research papers LangChain resources Hugging Face resources Alternative resources to LangChain Community and support Other important resources Conclusion Index