Decoding Gpt An Intuitive Understanding Of Large Language Models Generative Ai Machine Learning And Neural Networks

About the Book: In a world where Large Language Models (LLMs) like ChatGPT have ignited imaginations, individuals from all walks of life are eager to embrace the transformative potential of Generative AI. Whether you're a tech professional, decision-maker, an entrepreneur or a budding student, the pursuit of understanding this new paradigm is a shared endeavor. It's within this landscape that 'Decoding GPT: an Intuitive Introduction to LLMs' emerges as your essential guide. Now, as the author of "Decoding GPT," Devesh Rajadhyax invites you to join him on a journey into the heart of LLMs. This book starts with the fundamentals of machine learning and neural networks and then dives into the inner workings of Large Language Models, all while keeping complex math and programming at bay. Instead, it employs clear diagrams and relatable examples to foster a deep understanding. If your aim is to thrive in the world of generative AI, 'Decoding GPT' is your passport to a brighter future in this exciting field. About the Author: Devesh Rajadhyax is an entrepreneur and visionary communicator, whose life has been an unfolding journey of scientific curiosity and technological innovation. With a scientific curiosity that has defined him since childhood, Devesh has immersed himself in the ever-evolving realm of technology, leaving an indelible mark on the AI landscape through his brainchild, Cere Labs. Devesh’s journey is marked by an unwavering passion for technology that goes beyond the ordinary. He’s not just a leader; he’s a visionary who shapes the very course of innovation at Cere Labs. Beyond the boardroom, Devesh is a gifted writer, known for his insightful science and technology blogs and articles. His blog, “Yours Sciencely,” served as a welcoming space for those interested in delving into intricate scientific ideas presented in clear and eloquent language. His contributions to publications like “Towards Data Science” have been celebrated, demonstrating his prowess in translating complex ideas for a wider audience. Devesh’s influence extends to the academic world, where he has served on the Board of Studies for prestigious engineering colleges and universities. His warm rapport with professors and students in the technical realm, particularly computer engineering, has made him a respected figure in industry-academia interaction.

Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.

This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.

Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore the theory behind generative AI models and the road to GPT3 and GPT4 Become familiar with ChatGPT's applications to boost everyday productivity Learn to embed OpenAI models into applications using lightweight frameworks like LangChain Book Description Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects. What you will learn Understand generative AI concepts from basic to intermediate level Focus on the GPT architecture for generative AI models Maximize ChatGPT's value with an effective prompt design Explore applications and use cases of ChatGPT Use OpenAI models and features via API calls Build and deploy generative AI systems with Python Leverage Azure infrastructure for enterprise-level use cases Ensure responsible AI and ethics in generative AI systems Who this book is for This book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models. A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.

This book provides a comprehensive group of topics covering the details of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Spanning across ten chapters, it begins with foundational concepts such as the attention mechanism, then tokenization techniques, explores the nuances of Transformer and BERT architectures, and culminates in advanced topics related to the latest in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the intricacies of the Transformer architecture, a two-part exploration of the BERT family, and hands-on guidance on working with GPT-3. The concluding chapters present an overview of ChatGPT, GPT-4, and visualization using generative AI. In addition to the primary topics, the book also covers influential AI organizations such as DeepMind, OpenAI, Cohere, Hugging Face, and more. Readers will gain a comprehensive understanding of the current landscape of NLP models, their underlying architectures, and practical applications. Features companion files with numerous code samples and figures from the book. FEATURES: Provides a comprehensive group of topics covering the details of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Features companion files with numerous code samples and figures from the book.

GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API is a comprehensive book on the Generative Pre-trained Transformer 3 AI language model, covering its significance, capabilities, and application in creating innovative NLP Products. Key FeaturesExploration of GPT-3: The book explores GPT-3, a powerful language model, and its capabilitiesBusiness applications: The book provides practical knowledge on using GPT-3 to create new business productsExamination of AI trends: The book examines the impact of GPT-3 on emerging creator economy and trends like no-code & AGIBook Description GPT-3 has made creating AI apps simpler than ever. This book provides a comprehensive guide on how to utilize the OpenAI API with ease. It explores imaginative methods of utilizing this tool for your specific needs and showcases successful businesses that have been established through its use. The book is divided into two sections, with the first focusing on the fundamentals of the OpenAI API. The second part examines the dynamic and thriving environment that has arisen around GPT-3. Chapter 1 sets the stage with background information and defining key terms. Chapter 2 goes in-depth into the API, breaking it down into its essential components, explaining their functions and offering best practices. Chapter 3, you will build your first app with GPT-3. Chapter 4 features interviews with the founders of successful GPT-3-based products, who share challenges and insights gained. Chapter 5 examines the perspective of enterprises on GPT-3 and its potential for adoption. The problematic consequences of widespread GPT-3 adoption, such as misapplication and bias, are addressed along with efforts to resolve these issues in Chapter 6. Finally, Chapter 7 delves into the future by exploring the most exciting trends and possibilities as GPT-3 becomes increasingly integrated into the commercial ecosystem. What you will learnLearn the essential components of the OpenAI API along with the best practicesBuild and deploy your first GPT-3 powered applicationLearn from the journeys of industry leaders, startup founders who have built and deployed GPT-3 based products at scaleLook at how enterprises view GPT-3 and its potential for adoption for scalable solutionsNavigating the Consequences of GPT-3 adoption and efforts to resolve themExplore the exciting trends and possibilities of combining models with GPT-3 with No codeWho this book is for This book caters to individuals from diverse backgrounds, not just technical experts. It should be useful to you if you are:A data expert seeking to improve your AI expertiseAn entrepreneur looking to revolutionize the AI industryA business leader seeking to enhance your AI knowledge and apply it to informed decision makingA content creator in the language domain looking to utilize GPT-3's language abilities for creative and imaginative projectsAnyone with an AI idea that was previously deemed technically unfeasible or too costly to execute