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Generative Ai And Large Language Models
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Book Synopsis Generative Deep Learning by : David Foster
Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Book Synopsis Artificial Intelligence by : Melanie Mitchell
Download or read book Artificial Intelligence written by Melanie Mitchell and published by Farrar, Straus and Giroux. This book was released on 2019-10-15 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.
Book Synopsis Generative AI with Python and TensorFlow 2 by : Joseph Babcock
Download or read book Generative AI with Python and TensorFlow 2 written by Joseph Babcock and published by Packt Publishing Ltd. This book was released on 2021-04-30 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.
Book Synopsis Generative AI and LLMs by : S. Balasubramaniam
Download or read book Generative AI and LLMs written by S. Balasubramaniam and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-09-23 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
Book Synopsis The Executive Guide to Artificial Intelligence by : Andrew Burgess
Download or read book The Executive Guide to Artificial Intelligence written by Andrew Burgess and published by Springer. This book was released on 2017-11-15 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a pragmatic and hype–free approach to explaining artificial intelligence and how it can be utilised by businesses today. At the core of the book is a framework, developed by the author, which describes in non–technical language the eight core capabilities of Artificial Intelligence (AI). Each of these capabilities, ranging from image recognition, through natural language processing, to prediction, is explained using real–life examples and how they can be applied in a business environment. It will include interviews with executives who have successfully implemented AI as well as CEOs from AI vendors and consultancies. AI is one of the most talked about technologies in business today. It has the ability to deliver step–change benefits to organisations and enables forward–thinking CEOs to rethink their business models or create completely new businesses. But most of the real value of AI is hidden behind marketing hyperbole, confusing terminology, inflated expectations and dire warnings of ‘robot overlords’. Any business executive that wants to know how to exploit AI in their business today is left confused and frustrated. As an advisor in Artificial Intelligence, Andrew Burgess regularly comes face–to–face with business executives who are struggling to cut through the hype that surrounds AI. The knowledge and experience he has gained in advising them, as well as working as a strategic advisor to AI vendors and consultancies, has provided him with the skills to help business executives understand what AI is and how they can exploit its many benefits. Through the distilled knowledge included in this book business leaders will be able to take full advantage of this most disruptive of technologies and create substantial competitive advantage for their companies.
Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka
Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Book Synopsis Human + Machine by : Paul R. Daugherty
Download or read book Human + Machine written by Paul R. Daugherty and published by Harvard Business Press. This book was released on 2018-03-20 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.
Book Synopsis Generative AI and LLMs by : S. Balasubramaniam
Download or read book Generative AI and LLMs written by S. Balasubramaniam and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-09-23 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
Book Synopsis Generative AI with Large Language Models: A Comprehensive Guide by : Anand Vemula
Download or read book Generative AI with Large Language Models: A Comprehensive Guide written by Anand Vemula and published by Anand Vemula. This book was released on with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book delves into the fascinating world of Generative AI, exploring the two key technologies driving its advancements: Large Language Models (LLMs) and Foundation Models (FMs). Part 1: Foundations LLMs Demystified: We begin by understanding LLMs, powerful AI models trained on massive amounts of text data. These models can generate human-quality text, translate languages, write different creative formats, and even answer your questions in an informative way. The Rise of FMs: However, LLMs are just a piece of the puzzle. We explore Foundation Models, a broader category encompassing models trained on various data types like images, audio, and even scientific data. These models represent a significant leap forward in AI, offering a more versatile approach to information processing. Part 2: LLMs and Generative AI Applications Training LLMs: We delve into the intricate process of training LLMs, from data acquisition and pre-processing to different training techniques like supervised and unsupervised learning. The chapter also explores challenges like computational resources and data bias, along with best practices for responsible LLM training. Fine-Tuning for Specific Tasks: LLMs can be further specialized for targeted tasks through fine-tuning. We explore how fine-tuning allows LLMs to excel in areas like creative writing, code generation, drug discovery, and even music composition. Part 3: Advanced Topics LLM Architectures: We take a deep dive into the technical aspects of LLMs, exploring the workings of Transformer networks, the backbone of modern LLMs. We also examine the role of attention mechanisms in LLM processing and learn about different prominent LLM architectures like GPT-3 and Jurassic-1 Jumbo. Scaling Generative AI: Scaling up LLMs presents significant computational challenges. The chapter explores techniques like model parallelism and distributed training to address these hurdles, along with hardware considerations like GPUs and TPUs that facilitate efficient LLM training. Most importantly, we discuss the crucial role of safety and ethics in generative AI development. Mitigating bias, addressing potential risks like deepfakes, and ensuring transparency are all essential for responsible AI development. Part 4: The Future Evolving Generative AI Landscape: We explore emerging trends in LLM research, like the development of even larger and more capable models, along with advancements in explainable AI and the rise of multimodal LLMs that can handle different data types. We also discuss the potential applications of generative AI in unforeseen areas like personalized education and healthcare. Societal Impact and the Future of Work: The book concludes by examining the societal and economic implications of generative AI. We explore the potential transformation of industries, the need for workforce reskilling, and the importance of human-AI collaboration. Additionally, the book emphasizes the need for robust regulations to address concerns like bias, data privacy, and transparency in generative AI development. This book equips you with a comprehensive understanding of generative AI, its core technologies, its applications, and the considerations for its responsible development and deployment.
Book Synopsis Towards an International Political Economy of Artificial Intelligence by : Tugrul Keskin
Download or read book Towards an International Political Economy of Artificial Intelligence written by Tugrul Keskin and published by Springer Nature. This book was released on 2021-07-01 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume seeks to leverage academic interdisciplinarity to develop insight into how Artificial intelligence (AI), the latest GPT to emerge, may influence or radically change socio-political norms, practices, and institutions. AI may best be understood as a predictive technology. “Prediction is the process of filling in missing information. Prediction takes information you have, often called ‘data’, and uses it to generate information you don’t have” (Agrawal, Gans, and Goldfarb 2018, 13; also see Mayer-Schonberger and Ramge 2018). AI makes prediction cheap because the cost of information is now close to zero. Cheap prediction through AI technologies are radically altering how we govern ourselves, interact with each other, and sustain society. Contributors to this volume represent the academic disciplines of Sociology and Political Science working within a diverse set of intra-disciplinary fields that when combined, yield novel insights into the following questions guiding this volume: How might AI transform people? How might AI transform socio-political practices? How might AI transform socio-political institutions?
Book Synopsis Demystifying Large Language Models by : James Chen
Download or read book Demystifying Large Language Models written by James Chen and published by James Chen. This book was released on 2024-04-25 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture that powers Large Language Models (LLMs) like GPT and BERT. From PyTorch basics and mathematical foundations to implementing a Transformer from scratch, you'll gain a deep understanding of the inner workings of these models. That's just the beginning. Get ready to dive into the realm of pre-training your own Transformer from scratch, unlocking the power of transfer learning to fine-tune LLMs for your specific use cases, exploring advanced techniques like PEFT (Prompting for Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) for fine-tuning, as well as RLHF (Reinforcement Learning with Human Feedback) for detoxifying LLMs to make them aligned with human values and ethical norms. Step into the deployment of LLMs, delivering these state-of-the-art language models into the real-world, whether integrating them into cloud platforms or optimizing them for edge devices, this section ensures you're equipped with the know-how to bring your AI solutions to life. Whether you're a seasoned AI practitioner, a data scientist, or a curious developer eager to advance your knowledge on the powerful LLMs, this book is your ultimate guide to mastering these cutting-edge models. By translating convoluted concepts into understandable explanations and offering a practical hands-on approach, this treasure trove of knowledge is invaluable to both aspiring beginners and seasoned professionals. Table of Contents 1. INTRODUCTION 1.1 What is AI, ML, DL, Generative AI and Large Language Model 1.2 Lifecycle of Large Language Models 1.3 Whom This Book Is For 1.4 How This Book Is Organized 1.5 Source Code and Resources 2. PYTORCH BASICS AND MATH FUNDAMENTALS 2.1 Tensor and Vector 2.2 Tensor and Matrix 2.3 Dot Product 2.4 Softmax 2.5 Cross Entropy 2.6 GPU Support 2.7 Linear Transformation 2.8 Embedding 2.9 Neural Network 2.10 Bigram and N-gram Models 2.11 Greedy, Random Sampling and Beam 2.12 Rank of Matrices 2.13 Singular Value Decomposition (SVD) 2.14 Conclusion 3. TRANSFORMER 3.1 Dataset and Tokenization 3.2 Embedding 3.3 Positional Encoding 3.4 Layer Normalization 3.5 Feed Forward 3.6 Scaled Dot-Product Attention 3.7 Mask 3.8 Multi-Head Attention 3.9 Encoder Layer and Encoder 3.10 Decoder Layer and Decoder 3.11 Transformer 3.12 Training 3.13 Inference 3.14 Conclusion 4. PRE-TRAINING 4.1 Machine Translation 4.2 Dataset and Tokenization 4.3 Load Data in Batch 4.4 Pre-Training nn.Transformer Model 4.5 Inference 4.6 Popular Large Language Models 4.7 Computational Resources 4.8 Prompt Engineering and In-context Learning (ICL) 4.9 Prompt Engineering on FLAN-T5 4.10 Pipelines 4.11 Conclusion 5. FINE-TUNING 5.1 Fine-Tuning 5.2 Parameter Efficient Fine-tuning (PEFT) 5.3 Low-Rank Adaptation (LoRA) 5.4 Adapter 5.5 Prompt Tuning 5.6 Evaluation 5.7 Reinforcement Learning 5.8 Reinforcement Learning Human Feedback (RLHF) 5.9 Implementation of RLHF 5.10 Conclusion 6. DEPLOYMENT OF LLMS 6.1 Challenges and Considerations 6.2 Pre-Deployment Optimization 6.3 Security and Privacy 6.4 Deployment Architectures 6.5 Scalability and Load Balancing 6.6 Compliance and Ethics Review 6.7 Model Versioning and Updates 6.8 LLM-Powered Applications 6.9 Vector Database 6.10 LangChain 6.11 Chatbot, Example of LLM-Powered Application 6.12 WebUI, Example of LLM-Power Application 6.13 Future Trends and Challenges 6.14 Conclusion REFERENCES ABOUT THE AUTHOR
Book Synopsis The Generative AI Practitioner’s Guide by : Arup Das
Download or read book The Generative AI Practitioner’s Guide written by Arup Das and published by TinyTechMedia LLC. This book was released on 2024-07-20 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI is revolutionizing the way organizations leverage technology to gain a competitive edge. However, as more companies experiment with and adopt AI systems, it becomes challenging for data and analytics professionals, AI practitioners, executives, technologists, and business leaders to look beyond the buzz and focus on the essential questions: Where should we begin? How do we initiate the process? What potential pitfalls should we be aware of? This TinyTechGuide offers valuable insights and practical recommendations on constructing a business case, calculating ROI, exploring real-life applications, and considering ethical implications. Crucially, it introduces five LLM patterns—author, retriever, extractor, agent, and experimental—to effectively implement GenAI systems within an organization. The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications bridges critical knowledge gaps for business leaders and practitioners, equipping them with a comprehensive toolkit to define a business case and successfully deploy GenAI. In today’s rapidly evolving world, staying ahead of the competition requires a deep understanding of these five implementation patterns and the potential benefits and risks associated with GenAI. Designed for business leaders, tech experts, and IT teams, this book provides real-life examples and actionable insights into GenAI’s transformative impact on various industries. Empower your organization with a competitive edge in today’s marketplace using The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications. Remember, it’s not the tech that’s tiny, just the book!™
Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Book Synopsis Large Language Models by : Oswald Campesato
Download or read book Large Language Models written by Oswald Campesato and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-10-02 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher.
Book Synopsis Data Science on AWS by : Chris Fregly
Download or read book Data Science on AWS written by Chris Fregly and published by "O'Reilly Media, Inc.". This book was released on 2021-04-07 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Book Synopsis Linguistics for the Age of AI by : Marjorie Mcshane
Download or read book Linguistics for the Age of AI written by Marjorie Mcshane and published by MIT Press. This book was released on 2021-03-02 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.
Book Synopsis Artificial Intelligence in Practice by : Bernard Marr
Download or read book Artificial Intelligence in Practice written by Bernard Marr and published by John Wiley & Sons. This book was released on 2019-04-15 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.
