Deep Learning In Generative Ai From Fundamentals To Cutting Edge Applications

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Deep Learning in Generative AI: From Fundamentals to Cutting-Edge Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 153 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning in Generative AI: From Fundamentals to Cutting-Edge Applications by : Anand Vemula

Download or read book Deep Learning in Generative AI: From Fundamentals to Cutting-Edge Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth exploration of the foundational concepts, advanced techniques, and practical applications of generative AI, all powered by deep learning. The journey begins with a solid introduction to generative models, explaining their significance in AI and how they differ from discriminative models. It then covers the foundational elements of deep learning, including neural networks, backpropagation, activation functions, and optimization methods, laying the groundwork for understanding complex generative architectures. The book progresses to detailed discussions on various generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models. Each model is presented with its mathematical foundations, architecture, and step-by-step coding tutorials, making it accessible to both beginners and advanced practitioners. Real-world applications of these models are explored in depth, showcasing how generative AI is transforming industries like healthcare, finance, and creative arts. The book also addresses the challenges associated with training generative models, offering practical solutions and optimization techniques. Ethical considerations are a critical component, with dedicated sections on bias in generative models, deepfakes, and the implications of AI-generated content on intellectual property. The book concludes with a forward-looking discussion on future trends in generative AI, including the integration of AI with quantum computing and its role in promoting sustainability. With a balanced mix of theory, hands-on exercises, case studies, and practical examples, this book equips readers with the knowledge and tools to implement generative AI models in real-world scenarios, making it an essential resource for AI enthusiasts, researchers, and professionals.

Generative Deep Learning

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492041890
Total Pages : 301 pages
Book Rating : 4.4/5 (92 download)

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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

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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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

Azure OpenAI Service for Cloud Native Applications

Author : Adrián González Sánchez
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098154967
Total Pages : 249 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Azure OpenAI Service for Cloud Native Applications by : Adrián González Sánchez

Download or read book Azure OpenAI Service for Cloud Native Applications written by Adrián González Sánchez and published by "O'Reilly Media, Inc.". This book was released on 2024-06-27 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service—using powerful generative AI models such as GPT-4 and GPT-4o—within the Microsoft Azure cloud computing platform. To guide you through the technical details of using Azure OpenAI Service, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You'll learn various use cases where Azure OpenAI Service models can be applied, and get valuable insights from some of the most relevant AI and cloud experts. Ideal for software and cloud developers, product managers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you: Learn how to implement cloud native applications with Azure OpenAI Service Deploy, customize, and integrate Azure OpenAI Service with your applications Customize large language models and orchestrate knowledge with company-owned data Use advanced roadmaps to plan your generative AI project Estimate cost and plan generative AI implementations for adopter companies

Mastering AI and Generative AI: From Learning Fundamentals to Advanced Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 72 pages
Book Rating : 4./5 ( download)

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Book Synopsis Mastering AI and Generative AI: From Learning Fundamentals to Advanced Applications by : Anand Vemula

Download or read book Mastering AI and Generative AI: From Learning Fundamentals to Advanced Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide dives into the fascinating world of Artificial Intelligence (AI) and its cutting-edge subfield, Generative AI. Designed for beginners and enthusiasts alike, it equips you with the knowledge and skills to navigate the complexities of machine learning and unlock the power of AI for advanced applications. Building a Strong Foundation The journey begins with mastering the fundamentals. You'll explore the different approaches to AI, delve into the history of this revolutionary field, and gain a solid understanding of various subfields like Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Delving into Machine Learning Machine learning, the core of AI's learning ability, takes center stage. You'll grasp the difference between supervised and unsupervised learning paradigms, discover popular algorithms like decision trees and neural networks, and learn the importance of data preparation for optimal model performance. Evaluation metrics become your tools to measure how effectively your models are learning. Unveiling the Power of Deep Learning Get ready to explore the intricate world of Deep Learning, a powerful subset of machine learning inspired by the human brain. Demystify neural networks, the building blocks of deep learning, and dive into specialized architectures like Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for handling sequential data. Deep learning frameworks become your allies, simplifying the process of building and deploying complex deep learning models. The Art of Machine Creation: Generative AI The book then shifts its focus to the transformative realm of Generative AI. Here, machines not only learn but create entirely new data. Explore different types of generative models, from autoregressive models to variational autoencoders, and witness their applications in text generation, image synthesis, and even music creation. A Deep Dive into Generative Adversarial Networks (GANs) Among generative models, Generative Adversarial Networks (GANs) have captured the imagination of researchers and the public alike. This chapter delves into the intriguing concept of GANs, where a generator model continuously strives to create realistic data while a discriminator model acts as a critic, ensuring the generated data is indistinguishable from real data. You'll explore the training process, the challenges of taming GANs, and best practices for achieving optimal results. Advanced Applications Across Domains The book then showcases the transformative potential of Generative AI across various domains. Witness the power of text generation with RNNs, explore the ethical considerations surrounding deepfakes, and discover how chatbots are revolutionizing communication. In the visual realm, delve into Deep Dream and Neural Style Transfer algorithms, and witness the creation of realistic images and videos with cutting-edge generative models. Mastering AI and Generative AI empowers you to not only understand these revolutionary technologies but also leverage them for advanced applications. As you embark on this journey, be prepared to unlock the boundless potential of machine creation and shape the future of AI.

Google Machine Learning and Generative AI for Solutions Architects

Author : Kieran Kavanagh
Publisher : Packt Publishing Ltd
ISBN 13 : 1803247029
Total Pages : 552 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Google Machine Learning and Generative AI for Solutions Architects by : Kieran Kavanagh

Download or read book Google Machine Learning and Generative AI for Solutions Architects written by Kieran Kavanagh and published by Packt Publishing Ltd. This book was released on 2024-06-28 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively Key Features Understand key concepts, from fundamentals through to complex topics, via a methodical approach Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost companies today are incorporating AI/ML into their businesses. Building and running apps utilizing AI/ML effectively is tough. This book, authored by a principal architect with about two decades of industry experience, who has led cross-functional teams to design, plan, implement, and govern enterprise cloud strategies, shows you exactly how to design and run AI/ML workloads successfully using years of experience from some of the world’s leading tech companies. You’ll get a clear understanding of essential fundamental AI/ML concepts, before moving on to complex topics with the help of examples and hands-on activities. This will help you explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. You’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process. By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.What you will learn Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark Source, understand, and prepare data for ML workloads Build, train, and deploy ML models on Google Cloud Create an effective MLOps strategy and implement MLOps workloads on Google Cloud Discover common challenges in typical AI/ML projects and get solutions from experts Explore vector databases and their importance in Generative AI applications Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows Who this book is for This book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.

Learning Deep Learning

Author : Magnus Ekman
Publisher : Addison-Wesley Professional
ISBN 13 : 0137470290
Total Pages : 1106 pages
Book Rating : 4.1/5 (374 download)

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Book Synopsis Learning Deep Learning by : Magnus Ekman

Download or read book Learning Deep Learning written by Magnus Ekman and published by Addison-Wesley Professional. This book was released on 2021-07-19 with total page 1106 pages. Available in PDF, EPUB and Kindle. Book excerpt: NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Machine Learning for High-Risk Applications

Author : Patrick Hall
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098102401
Total Pages : 469 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Machine Learning for High-Risk Applications by : Patrick Hall

Download or read book Machine Learning for High-Risk Applications written by Patrick Hall and published by "O'Reilly Media, Inc.". This book was released on 2023-04-17 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab

The Pioneering Applications of Generative AI

Author : Kumar, Raghvendra
Publisher : IGI Global
ISBN 13 :
Total Pages : 360 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis The Pioneering Applications of Generative AI by : Kumar, Raghvendra

Download or read book The Pioneering Applications of Generative AI written by Kumar, Raghvendra and published by IGI Global. This book was released on 2024-07-17 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrating generative artificial intelligence (AI) into art, design, and media presents a double-edged sword. While it offers unprecedented creative possibilities, it raises ethical concerns, challenges traditional workflows, and requires careful regulation. As AI becomes more prevalent in these fields, there is a pressing need for a comprehensive resource that explores the technology's potential and navigates the complex landscape of its implications. The Pioneering Applications of Generative AI is a pioneering book that addresses these challenges head-on. It provides a deep dive into the evolution, ethical considerations, core technologies, and creative applications of generative AI, offering readers a thorough understanding of this transformative technology. Researchers, academicians, scientists, and research scholars will find this book invaluable in navigating the complexities of generative AI in art, design, and media. With its focus on ethical and responsible AI and discussions on regulatory frameworks, the book equips readers with the knowledge and tools needed to harness the full potential of generative AI while ensuring its responsible and ethical use.

Deep Learning with PyTorch

Author : Luca Pietro Giovanni Antiga
Publisher : Simon and Schuster
ISBN 13 : 1638354073
Total Pages : 518 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning with PyTorch by : Luca Pietro Giovanni Antiga

Download or read book Deep Learning with PyTorch written by Luca Pietro Giovanni Antiga and published by Simon and Schuster. This book was released on 2020-07-01 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: “We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Generative AI on AWS

Author : Chris Fregly
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098159187
Total Pages : 323 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Generative AI on AWS by : Chris Fregly

Download or read book Generative AI on AWS written by Chris Fregly and published by "O'Reilly Media, Inc.". This book was released on 2023-11-13 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock

Microsoft Azure AI Fundamentals AI-900 Exam Guide

Author : Aaron Guilmette
Publisher : Packt Publishing Ltd
ISBN 13 : 1835885675
Total Pages : 288 pages
Book Rating : 4.8/5 (358 download)

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Book Synopsis Microsoft Azure AI Fundamentals AI-900 Exam Guide by : Aaron Guilmette

Download or read book Microsoft Azure AI Fundamentals AI-900 Exam Guide written by Aaron Guilmette and published by Packt Publishing Ltd. This book was released on 2024-05-31 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key Features Discover Azure AI services, including computer vision, Auto ML, NLP, and OpenAI Explore AI use cases, such as image identification, chatbots, and more Work through 145 practice questions under chapter-end self-assessments and mock exams Purchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tips Book Description The AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You'll explore Microsoft's responsible AI principles like safety and accountability. Then, you'll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you'll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you'll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you'll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources. What you will learn Discover various types of artificial intelligence (AI)workloads and services in Azure Cover Microsoft's guiding principles for responsible AI development and use Understand the fundamental principles of how AI and machine learning work Explore how AI models can recognize content in images and documents Gain insights into the features and use cases for natural language processing Explore the capabilities of generative AI services Who this book is for Whether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed.

DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED

Author : Siddharth Konkimalla
Publisher : BUDHA PUBLISHER
ISBN 13 : 9361756079
Total Pages : 192 pages
Book Rating : 4.3/5 (617 download)

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Book Synopsis DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED by : Siddharth Konkimalla

Download or read book DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED written by Siddharth Konkimalla and published by BUDHA PUBLISHER. This book was released on with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: .The advances in data engineering technologies, including big data infrastructure, knowledge graphs, and mechanism design, will have a long-lasting impact on artificial intelligence (AI) research and development. This paper introduces data engineering in AI with a focus on the basic concepts, applications, and emerging frontiers. As a new research field, most data engineering in AI is yet to be properly defined, and there are abundant problems and applications to be explored. The primary purpose of this paper is to expose the AI community to this shining star of data science, stimulate AI researchers to think differently and form a roadmap of data engineering for AI. Since this is primarily an informal essay rather than an academic paper, its coverage is limited. The vast majority of the stimulating studies and ongoing projects are not mentioned in the paper.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Advanced Applications of Generative AI and Natural Language Processing Models

Author : Obaid, Ahmed J.
Publisher : IGI Global
ISBN 13 :
Total Pages : 505 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Advanced Applications of Generative AI and Natural Language Processing Models by : Obaid, Ahmed J.

Download or read book Advanced Applications of Generative AI and Natural Language Processing Models written by Obaid, Ahmed J. and published by IGI Global. This book was released on 2023-12-21 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid advancements in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and Generative AI, pose a challenge for academic scholars. Staying current with the latest techniques and applications in these fields is difficult due to their dynamic nature, while the lack of comprehensive resources hinders scholars' ability to effectively utilize these technologies. Advanced Applications of Generative AI and Natural Language Processing Models offers an effective solution to address these challenges. This comprehensive book delves into cutting-edge developments in NLP and Generative AI. It provides insights into the functioning of these technologies, their benefits, and associated challenges. Targeting students, researchers, and professionals in AI, NLP, and computer science, this book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI. By providing real-world examples and practical applications, scholars can apply their learnings to solve complex problems across various domains. Embracing Advanced Applications of Generative AI and Natural Language Processing Modelsequips academic scholars with the necessary knowledge and insights to explore innovative applications and unleash the full potential of generative AI and NLP models for effective problem-solving.

Generative Deep Learning with Python

Author : Cuantum Technologies LLC
Publisher : Packt Publishing Ltd
ISBN 13 : 1836207123
Total Pages : 276 pages
Book Rating : 4.8/5 (362 download)

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Book Synopsis Generative Deep Learning with Python by : Cuantum Technologies LLC

Download or read book Generative Deep Learning with Python written by Cuantum Technologies LLC and published by Packt Publishing Ltd. This book was released on 2024-06-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications. Key Features Comprehensive coverage of deep learning and generative models. In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI. Practical coding exercises & interactive assignments to build your own generative models. Book DescriptionGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.What you will learn Develop a detailed understanding of deep learning fundamentals Implement and train Generative Adversarial Networks (GANs) Create & utilize Variational Autoencoders for data generation Apply autoregressive models for text generation Explore advanced topics & stay ahead in the field of generative AI Analyze and optimize the performance of generative models Who this book is for This course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial.

AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

Author : Somanath Nanda
Publisher : Packt Publishing Ltd
ISBN 13 : 1835082904
Total Pages : 343 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide by : Somanath Nanda

Download or read book AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide written by Somanath Nanda and published by Packt Publishing Ltd. This book was released on 2024-02-29 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prepare confidently for the AWS MLS-C01 certification with this comprehensive and up-to-date exam guide, accompanied by web-based tools such as mock exams, flashcards, and exam tips Key Features Gain proficiency in AWS machine learning services to excel in the MLS-C01 exam Build model training and inference pipelines and deploy machine learning models to the AWS cloud Practice on the go with the mobile-friendly bonus website, accessible with the book Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe AWS Certified Machine Learning Specialty (MLS-C01) exam evaluates your ability to execute machine learning tasks on AWS infrastructure. This comprehensive book aligns with the latest exam syllabus, offering practical examples to support your real-world machine learning projects on AWS. Additionally, you'll get lifetime access to supplementary online resources, including mock exams with exam-like timers, detailed solutions, interactive flashcards, and invaluable exam tips, all accessible across various devices—PCs, tablets, and smartphones. Throughout the book, you’ll learn data preparation techniques for machine learning, covering diverse methods for data manipulation and transformation across different variable types. Addressing challenges such as missing data and outliers, the book guides you through an array of machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, accompanied by requisite machine learning algorithms essential for exam success. The book helps you master the deployment of models in production environments and their subsequent monitoring. Equipped with insights from this book and the accompanying mock exams, you'll be fully prepared to achieve the AWS MLS-C01 certification.What you will learn Identify ML frameworks for specific tasks Apply CRISP-DM to build ML pipelines Combine AWS services to build AI/ML solutions Apply various techniques to transform your data, such as one-hot encoding, binary encoder, ordinal encoding, binning, and text transformations Visualize relationships, comparisons, compositions, and distributions in the data Use data preparation techniques and AWS services for batch and real-time data processing Create training and inference ML pipelines with Sage Maker Deploy ML models in a production environment efficiently Who this book is for This book is designed for both students and professionals preparing for the AWS Certified Machine Learning Specialty exam or enhance their understanding of machine learning, with a specific emphasis on AWS. Familiarity with machine learning basics and AWS services is recommended to fully benefit from this book.