Mastering Generative Ai And Data Engineering

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Mastering Generative AI and Data Engineering

Author : Anand Vemula
Publisher : Independently Published
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.3/5 (27 download)

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Book Synopsis Mastering Generative AI and Data Engineering by : Anand Vemula

Download or read book Mastering Generative AI and Data Engineering written by Anand Vemula and published by Independently Published. This book was released on 2024-05-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mastering Generative AI and Data Engineering: Techniques, Tools, and Practical Applications" is a comprehensive guide designed for professionals, researchers, and enthusiasts eager to delve into the world of generative AI and data engineering. This book offers an in-depth exploration of the fundamental concepts, cutting-edge techniques, and essential tools needed to build, scale, and secure generative AI solutions effectively. Starting with an introduction to generative AI, the book covers its historical evolution, key terminologies, and diverse applications, such as image synthesis, text generation, and music composition. It delves into ethical considerations, highlighting the importance of responsible AI practices. Readers will gain a solid foundation in data engineering fundamentals, including data collection, storage, processing, and governance, essential for supporting robust AI models. The book progresses to designing and implementing data pipelines, comparing real-time and batch processing strategies, and utilizing orchestration tools like Apache Airflow and Kafka. It emphasizes the importance of monitoring and maintaining data pipelines to ensure reliable performance. A significant portion of the book is dedicated to deep learning techniques, including neural network architectures, training methodologies, and advanced generative models like GANs, VAEs, and reinforcement learning. It also explores state-of-the-art models such as transformers and hybrid generative techniques, offering practical code examples and use cases. Chapters on project implementation guide readers through planning, data requirements, model selection, and evaluation metrics. Real-world case studies illustrate successful generative AI deployments, emphasizing scalability and performance optimization. The book also addresses security and privacy concerns, providing strategies to safeguard data and models against adversarial attacks. To equip readers with the best tools and frameworks, the book reviews popular data engineering tools like Apache Hadoop and Spark, AI frameworks like TensorFlow and PyTorch, and cloud platforms like AWS and Google Cloud. Integration strategies for seamless data engineering and AI tool usage are also covered. Finally, the book looks to the future, discussing emerging trends, advances in data engineering, AI integration with IoT and edge computing, and the evolving landscape of human-AI collaboration. This guide is an indispensable resource for anyone aiming to master generative AI and data engineering

Mastering Generative AI Software Development

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4./5 ( download)

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Book Synopsis Mastering Generative AI Software Development by : Anand Vemula

Download or read book Mastering Generative AI Software Development written by Anand Vemula and published by Anand Vemula. This book was released on with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mastering Generative AI Software Development equips you to harness the power of generative models, a revolutionary technology capable of creating entirely new and original content. Part 1 establishes a solid foundation. You'll explore the core concepts of generative models, contrasting them with traditional machine learning approaches. We'll delve into the exciting applications of generative AI, from creative content generation like writing and music composition to scientific breakthroughs in drug discovery and material science. The section concludes by discussing both the benefits and challenges associated with this powerful technology. Part 2 guides you through the practical steps of building generative AI systems. We'll tackle data preparation, a crucial stage for ensuring high-quality model training. You'll learn about different data cleaning and augmentation techniques to optimize your data for generative models. Moving on, we'll explore various generative model architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The section delves into the training process, equipping you with the knowledge to choose appropriate loss functions and optimizers for optimal model performance. We'll also explore techniques for monitoring and debugging the training process to ensure successful model development. Part 3 showcases the vast potential of generative AI software across diverse industries. We'll explore how generative models are revolutionizing creative fields, enabling artists and writers to generate new content and explore innovative avenues. Beyond the realm of creativity, we'll delve into the transformative role of generative AI in scientific research, accelerating drug discovery and material design processes. The section concludes by exploring additional applications like data augmentation and natural language processing tasks such as machine translation and chatbot development. Part 4 paves the way for the future. We'll discuss the ethical considerations surrounding generative AI development, particularly the potential for bias and the misuse of realistic content generation. The section concludes by exploring cutting-edge advancements like explainable generative models and the ever-expanding real-world applications of this technology. By the end of this comprehensive guide, you'll possess a thorough understanding of generative AI software development, empowering you to participate in shaping the future of this rapidly evolving field.

Mastering Prompt Engineering for Generative AI: Unlocking the Full Potential of AI Technology

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4./5 ( download)

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Book Synopsis Mastering Prompt Engineering for Generative AI: Unlocking the Full Potential of AI Technology by : Anand Vemula

Download or read book Mastering Prompt Engineering for Generative AI: Unlocking the Full Potential of AI Technology written by Anand Vemula and published by Anand Vemula. This book was released on with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to mastering the art and science of crafting effective prompts to unleash the full potential of generative AI. This book delves into the principles, techniques, and ethical considerations of prompt engineering, providing readers with practical insights and strategies for achieving optimal results. The book begins by exploring the fundamentals of prompt engineering, including the definition of prompts, their importance in guiding AI models, and the various types of prompts commonly used. Readers will learn about the common challenges and pitfalls of prompt engineering and gain a deeper understanding of AI models, their architecture, training processes, strengths, and limitations. Moving on to advanced topics, the book covers multi-turn prompts, contextual and sequential prompts, and leveraging model outputs to refine and enhance AI-generated content. Readers will discover domain-specific applications of prompt engineering, including creative writing, conversational AI, visual and artistic applications, and scientific and technical domains. Ethical considerations play a significant role in prompt engineering, and the book provides insights into recognizing and mitigating bias, ethical considerations, and responsible AI use. Readers will also gain practical knowledge of the tools and platforms available for prompt engineering, along with techniques for customizing and extending AI models. Testing and evaluation are essential aspects of prompt engineering, and the book offers guidance on metrics for assessing prompt performance, gathering user feedback, and conducting A/B testing and comparative analysis. Real-world case studies highlight successful applications of prompt engineering across various industries, offering valuable lessons and insights. Finally, the book explores future trends in generative AI and prompt engineering, providing readers with a glimpse into emerging technologies, predictions for the future of AI, and strategies for preparing for future developments. With its comprehensive coverage and practical insights, "Prompt Engineering for Generative AI" is an indispensable resource for AI enthusiasts, researchers, developers, and anyone looking to harness the power of generative AI through effective prompt engineering

AI for Beginners

Author : Alan Miller
Publisher : Independently Published
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.8/5 (848 download)

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Book Synopsis AI for Beginners by : Alan Miller

Download or read book AI for Beginners written by Alan Miller and published by Independently Published. This book was released on 2024-03-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: GET YOU BONUS: MASTERING GBT'S: A guide within a guide where we delve into practical tips and advanced techniques to ensure you get the most out of ChatGPT. Are you intrigued by the power of ai? Do you wonder how machines learn to generate content that feels uniquely human? Are you looking to deepen your understanding in the evolving field of generative ai? "AI FOR BEGINNERS is your essential resource into the world of artificial intelligence. This book demystifies the complex concepts of Generative AI, making them accessible to anyone with curiosity and a desire to explore the future of creativity and innovation. Within these pages, you'll discover: An In-Depth Exploration of AI and Generative AI: Gain a comprehensive understanding of the fundamentals and the core technologies and theories that enable the magic of Generative AI. Practical Applications: Discover the transformative impact of Generative AI across art, music, video production, data analysis, coding and beyond. Hands-On Guidance: This book not only teaches you how AI works but also how to apply it effectively, offering practical advice and step by step guidance for leveraging Generative AI in your life and projects. Ethical Insights: Learn about the responsible use of AI, addressing the moral implications of AI-generated content and its influence on our digital landscape. Whether you're an aspiring developer, an artist intrigued by the possibilities of AI, or simply someone fascinated by the capability of technology to emulate and augment human creativity, this book offers a comprehensive and insightful exploration of Generative AI. This book is not just a guide; it's a gateway to the future. Are you ready to transform curiosity into comprehensive knowledge and ideas into tangible outcomes? Grab you copy and begin your journey!

200 Tips for Mastering Generative AI

Author : Rick Spair
Publisher : Rick Spair
ISBN 13 :
Total Pages : 888 pages
Book Rating : 4./5 ( download)

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Book Synopsis 200 Tips for Mastering Generative AI by : Rick Spair

Download or read book 200 Tips for Mastering Generative AI written by Rick Spair and published by Rick Spair. This book was released on with total page 888 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the rapidly evolving landscape of artificial intelligence, Generative AI stands out as a transformative force with the potential to revolutionize industries and reshape our understanding of creativity and automation. From its inception, Generative AI has captured the imagination of researchers, developers, and entrepreneurs, offering unprecedented capabilities in generating new data, simulating complex systems, and solving intricate problems that were once considered beyond the reach of machines. This book, "200 Tips for Mastering Generative AI," is a comprehensive guide designed to empower you with the knowledge and practical insights needed to harness the full potential of Generative AI. Whether you are a seasoned AI practitioner, a curious researcher, a forward-thinking entrepreneur, or a passionate enthusiast, this book provides valuable tips and strategies to navigate the vast and intricate world of Generative AI. We invite you to explore, experiment, and innovate with the knowledge you gain from this book. Together, we can unlock the full potential of Generative AI and shape a future where intelligent machines and human creativity coexist and collaborate in unprecedented ways. Welcome to "200 Tips for Mastering Generative AI." Your journey into the fascinating world of Generative AI begins here.

AI Unraveled - Master GPT-x, Gemini, Generative AI, LLMs, Prompt Engineering: A simplified Guide For Everyday Users

Author : Etienne Noumen
Publisher : Etienne Noumen
ISBN 13 :
Total Pages : 147 pages
Book Rating : 4./5 ( download)

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Book Synopsis AI Unraveled - Master GPT-x, Gemini, Generative AI, LLMs, Prompt Engineering: A simplified Guide For Everyday Users by : Etienne Noumen

Download or read book AI Unraveled - Master GPT-x, Gemini, Generative AI, LLMs, Prompt Engineering: A simplified Guide For Everyday Users written by Etienne Noumen and published by Etienne Noumen. This book was released on with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the revolutionary world of Artificial Intelligence with 'AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence'. This comprehensive guide is your portal to understanding AI's most intricate concepts and cutting-edge developments. Whether you're a curious beginner or an AI enthusiast, this book is tailored to unveil the complexities of AI in a simple, accessible manner. What's Inside: Fundamental AI Concepts: Journey through the basics of AI, machine learning, deep learning, and neural networks. AI in Action: Explore how AI is reshaping industries and society, diving into its applications in computer vision, natural language processing, and beyond. Ethical AI: Tackle critical issues like AI ethics and bias, understanding the moral implications of AI advancements. Industry Insights: Gain insights into how AI is revolutionizing industries and impacting our daily lives. The Future of AI: Forecast the exciting possibilities and challenges that lie ahead in the AI landscape. Special Focus on Generative AI & LLMs: Latest AI Trends: Stay updated with the latest in AI, including ChatGPT, Google Bard, GPT-4, Gemini, and more. Interactive Quizzes: Test your knowledge with engaging quizzes on Generative AI and Large Language Models (LLMs). Practical Guides: Master GPT-4 with a simplified guide, delve into advanced prompt engineering, and explore the nuances of temperature settings in AI. Real-World Applications: Learn how to leverage AI in various sectors, from healthcare to cybersecurity, and even explore its potential in areas like aging research and brain implants. For the AI Enthusiast: Prompt Engineering: Uncover secrets to crafting effective prompts for ChatGPT/Google Bard. AI Career Insights: Explore lucrative career paths in AI, including roles like AI Prompt Engineers. AI Investment Guide: Navigate the world of AI stocks and investment opportunities. Your Guide to Navigating AI: Do-It-Yourself Tutorials: From building custom ChatGPT applications to running LLMs locally, this book offers step-by-step guides. AI for Everyday Use: Learn how AI can assist in weight loss, social media, and more. 'AI Unraveled' is more than just a book; it's a resource for anyone looking to grasp the complexities of AI and its impact on our world. Get ready to embark on an enlightening journey into the realm of Artificial Intelligence!" More Topics Covered: Artificial Intelligence, Machine Learning, Deep Learning, NLP, AI Ethics, Robotics, Cognitive Computing, ChatGPT, OpenAI, Google Bard, Generative AI, LLMs, AI in Healthcare, AI Investments, and much more. GPT-4 vs Gemini: Pros and Cons Mastering GPT-4: Simplified Guide For everyday Users Advance Prompt Engineering Techniques: [Single Prompt Technique, Zero-Shot and Few-Shot, Zero-Shot and Few-Shot, Generated Knowledge Prompting, EmotionPrompt, Chain of Density (CoD), Chain of Thought (CoT), Validation of LLMs Responses, Chain of Verification (CoVe), Agents - The Frontier of Prompt Engineering, Prompt Chaining vs Agents, Tree of Thought (ToT), ReAct (Reasoning + Act), ReWOO (Reasoning WithOut Observation), Reflexion and Self-Reflection, Guardrails, RAIL (Reliable AI Markup Language), Guardrails AI, NeMo Guardrails] Understanding Temperature in GPT-4: A Guide to AI Probability and Creativity Retrieval-Augmented Generation (RAG) model in the context of Large Language Models (LLMs) like GPT-4 Prompt Ideas for ChatGPT/Google Bard How to Run ChatGPT-like LLMs Locally on Your Computer in 3 Easy Steps ChatGPT Custom Instructions Settings for Power Users Examples of bad and good ChatGPT prompts Top 5 Beginner Mistakes in Prompt Engineering Use ChatGPT like a PRO Prompt template for learning any skill Prompt Engineering for ChatGPT The Future of LLMs in Search What is Explainable AI? Which industries are meant for XAI? ChatGPT Best Tips, Cheat Sheet LLMs Utilize Vector DB for Data Storage The Limitation Technique in Prompt Responses Use ChatGPT to learn new subjects Prompts to proofread anything Topics: Artificial Intelligence Education Machine Learning Deep Learning Reinforcement Learning Neural networks Data science AI ethics Deepmind Robotics Natural language processing Intelligent agents Cognitive computing AI Apps AI impact AI Tech ChatGPT Open AI Safe AI Generative AI Discriminative AI Sam Altman Google Bard NVDIA Large Language Models (LLMs) PALM GPT Explainable AI GPUs AI Stocks AI Podcast Q* AI Certification AI Quiz RAG How to access the AI Unraveled print and audiobook: Amazon print book: https://amzn.to/3xvCfWR Audible at Amazon : https://www.audible.com/pd/B0BXMJ7FK5/?source_code=AUDFPWS0223189MWT-BK-ACX0-343437&ref=acx_bty_BK_ACX0_343437_rh_us (Use Promo code: 37YT3B5UYUYZW) Audiobook at Google: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM Amazon eBook: https://amzn.to/3KbshkO Google eBook: https://play.google.com/store/books/details?id=oySuEAAAQBAJ Apple eBook: http://books.apple.com/us/book/id6445730691

Generative AI with Python and TensorFlow

Author : Anand Vemula
Publisher : Independently Published
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.3/5 (321 download)

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Book Synopsis Generative AI with Python and TensorFlow by : Anand Vemula

Download or read book Generative AI with Python and TensorFlow written by Anand Vemula and published by Independently Published. This book was released on 2024-07-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models is a comprehensive resource for anyone looking to delve into the world of generative artificial intelligence. Introduction Overview of Generative AI: Understand the basic concepts, history, and significance of generative AI. Importance of Generative AI: Learn about the transformative potential of generative AI in various industries. Applications and Use Cases: Explore real-world applications of generative AI in fields such as art, music, text generation, and data augmentation. Overview of Python and TensorFlow: Get an introduction to the essential tools and libraries used for building generative AI models. Getting Started: Set up your development environment, install necessary libraries, and take your first steps with TensorFlow. Fundamentals of Machine Learning Supervised vs. Unsupervised Learning: Understand the differences and use cases of these two primary types of machine learning. Neural Networks Basics: Learn the fundamental concepts of neural networks and their role in AI. Introduction to Deep Learning: Dive deeper into the advanced techniques of deep learning and its applications in generative AI. Key Concepts in Generative AI: Familiarize yourself with the essential concepts and terminologies in generative AI. Generative Models Understanding Generative Models: Explore the theoretical foundations of generative models. Types of Generative Models: Learn about various types of generative models, including VAEs, GANs, autoregressive models, and flow-based models. Variational Autoencoders (VAEs): Delve into the theory behind VAEs, build and train VAEs with TensorFlow, and explore their use cases. Generative Adversarial Networks (GANs): Get introduced to GANs, understand their architecture, implement GANs with TensorFlow, and learn advanced GAN techniques. Autoregressive Models: Understand autoregressive models, implement them with TensorFlow, and explore their applications. Flow-based Models: Learn about flow-based models, build them with TensorFlow, and explore their practical applications. Advanced Topics Transfer Learning for Generative Models: Explore how transfer learning can be applied to generative models. Conditional Generative Models: Understand and implement models that generate outputs conditioned on specific inputs. Multimodal Generative Models: Learn about models that can generate multiple types of data simultaneously. Reinforcement Learning in Generative AI: Explore the intersection of reinforcement learning and generative AI. Practical Applications Image Generation and Style Transfer: Create stunning images and apply style transfer techniques. Text Generation and Natural Language Processing: Generate coherent and contextually relevant text using advanced NLP techniques. Music and Sound Generation: Compose music and generate new sounds using generative AI. Data Augmentation for Machine Learning: Improve your machine learning models by augmenting your datasets with generative models. Hands-On Projects Project 1: Creating Art with GANs: Step-by-step guide to building a GAN to generate art. Project 2: Text Generation with LSTM: Implement an LSTM model for generating text. Project 3: Building a VAE for Image Reconstruction: Learn how to build and train a VAE for image reconstruction. Project 4: Music Generation with RNNs: Create a music generation model using RNNs.

Data Labeling in Machine Learning with Python

Author : Vijaya Kumar Suda
Publisher : Packt Publishing Ltd
ISBN 13 : 1804613789
Total Pages : 398 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Data Labeling in Machine Learning with Python by : Vijaya Kumar Suda

Download or read book Data Labeling in Machine Learning with Python written by Vijaya Kumar Suda and published by Packt Publishing Ltd. This book was released on 2024-01-31 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling Key Features Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.

Generative Deep Learning

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 109813415X
Total Pages : 456 pages
Book Rating : 4.0/5 (981 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 2022-06-28 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.

Mastering TensorFlow 1.x

Author : Armando Fandango
Publisher : Packt Publishing Ltd
ISBN 13 : 1788297008
Total Pages : 464 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Mastering TensorFlow 1.x by : Armando Fandango

Download or read book Mastering TensorFlow 1.x written by Armando Fandango and published by Packt Publishing Ltd. This book was released on 2018-01-22 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Book Description TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems. What you will learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters Who this book is for This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.

Mastering Deep Learning

Author : Cybellium Ltd
Publisher : Cybellium Ltd
ISBN 13 :
Total Pages : 240 pages
Book Rating : 4.8/5 (75 download)

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Book Synopsis Mastering Deep Learning by : Cybellium Ltd

Download or read book Mastering Deep Learning written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Power of Neural Networks for Intelligent Solutions In the landscape of artificial intelligence and machine learning, deep learning stands as a revolutionary force that is shaping the future of technology. "Mastering Deep Learning" is your ultimate guide to comprehending and harnessing the potential of deep neural networks, empowering you to create intelligent solutions that drive innovation. About the Book: As the capabilities of technology expand, deep learning emerges as a transformative approach that unlocks the potential of artificial intelligence. "Mastering Deep Learning" offers a comprehensive exploration of this cutting-edge field—an indispensable toolkit for data scientists, engineers, and enthusiasts. This book caters to both beginners and experienced learners aiming to excel in deep learning concepts, algorithms, and applications. Key Features: Deep Learning Fundamentals: Begin by understanding the core principles of deep learning. Learn about neural networks, activation functions, and backpropagation—the building blocks of the subject. Deep Neural Architectures: Dive into the world of deep neural architectures. Explore techniques for building and designing different types of neural networks, including feedforward, convolutional, and recurrent networks. Training and Optimization: Grasp the art of training deep neural networks. Understand techniques for weight initialization, gradient descent, and optimization algorithms to ensure efficient learning. Natural Language Processing: Explore deep learning applications in natural language processing. Learn how to process and understand text, sentiment analysis, and language generation. Computer Vision: Understand the significance of deep learning in computer vision. Explore techniques for image classification, object detection, and image generation. Reinforcement Learning: Delve into the realm of reinforcement learning. Explore techniques for training agents to interact with environments and make intelligent decisions. Transfer Learning and Pretrained Models: Grasp the power of transfer learning. Learn how to leverage pretrained models and adapt them to new tasks. Real-World Applications: Gain insights into how deep learning is applied across industries. From healthcare to finance, discover the diverse applications of deep neural networks. Why This Book Matters: In an era of rapid technological advancement, mastering deep learning offers a competitive edge. "Mastering Deep Learning" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge concepts, enabling them to create intelligent solutions that drive innovation and redefine possibilities. Unleash the Future of AI: In the landscape of artificial intelligence, deep learning is reshaping technology and innovation. "Mastering Deep Learning" equips you with the knowledge needed to leverage deep neural networks, enabling you to create intelligent solutions that push the boundaries of possibilities. Whether you're a seasoned practitioner or new to the world of deep learning, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering deep learning starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Generative Deep Learning

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492041890
Total Pages : 360 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 360 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

Generative AI Foundations in Python

Author : Carlos Rodriguez
Publisher :
ISBN 13 : 9781835460825
Total Pages : 0 pages
Book Rating : 4.4/5 (68 download)

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Book Synopsis Generative AI Foundations in Python by : Carlos Rodriguez

Download or read book Generative AI Foundations in Python written by Carlos Rodriguez and published by . This book was released on 2024-07-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features: - Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation - Use transformers-based LLMs and diffusion models to implement AI applications - Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems - Purchase of the print or Kindle book includes a free PDF eBook Book Description: The intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You'll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you'll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you'll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly. What You Will Learn: - Discover the fundamentals of GenAI and its foundations in NLP - Dissect foundational generative architectures including GANs, transformers, and diffusion models - Find out how to fine-tune LLMs for specific NLP tasks - Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance - Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG - Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for: This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected. Table of Contents - Understanding Generative AI: An Introduction - Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers - Tracing the Foundations of Natural Language Processing and the Impact of the Transformer - Applying Pretrained Generative Models: From Prototype to Production - Fine-Tuning Generative Models for Specific Tasks - Understanding Domain Adaptation for Large Language Models - Mastering the Fundamentals of Prompt Engineering - Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI

Generative AI with LangChain

Author : Ben Auffarth
Publisher : Packt Publishing Ltd
ISBN 13 : 1835088368
Total Pages : 369 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis Generative AI with LangChain by : Ben Auffarth

Download or read book Generative AI with LangChain written by Ben Auffarth and published by Packt Publishing Ltd. This book was released on 2023-12-22 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: 2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Data Mining and Machine Learning

Author : Mohammed J. Zaki
Publisher : Cambridge University Press
ISBN 13 : 1108473989
Total Pages : 779 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Data Mining and Machine Learning by : Mohammed J. Zaki

Download or read book Data Mining and Machine Learning written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2020-01-30 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

GenAI Black Book

Author : Husn Ara
Publisher : Independently Published
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.3/5 (265 download)

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Book Synopsis GenAI Black Book by : Husn Ara

Download or read book GenAI Black Book written by Husn Ara and published by Independently Published. This book was released on 2024-05-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the "GenAI Black Book", an extensive guide crafted to unravel the fascinating world of Generative Artificial Intelligence (GenAI). As we stand on the cusp of a technological revolution, the ability of AI to create, innovate, and transform is becoming increasingly profound. This book is designed to be your comprehensive companion on this journey, whether you are a novice taking your first steps into AI or an experienced practitioner looking to deepen your understanding. The Dawn of Generative AI Generative AI represents a pivotal advancement in the field of artificial intelligence, enabling machines to generate text, images, music, and more, with creativity and coherence that often rival human outputs. From generating realistic images to crafting compelling narratives, the applications of GenAI are as diverse as they are groundbreaking. This book aims to demystify these technologies, providing clear explanations, practical examples, and insightful discussions on their implications. The book begins with an introduction to the fundamental concepts of generative AI, providing readers with a solid understanding of the underlying principles and methodologies. It covers various generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, elucidating their architectures, training procedures, and real-world use cases. As readers progress through the chapters, they are introduced to advanced topics such as conditional generation, multimodal generation, unsupervised learning, LLM, LLAMA, Prompt Engineering and Retrieval-Augmented Generation (RAG) Framework, gaining hands-on experience through code examples and tutorials. The book also explores cutting-edge research and emerging trends in the field, offering insights into the future directions of generative AI. With practical guidance and actionable insights, "GenAI Black Book " equips readers with the knowledge and skills needed to harness the power of generative AI for a wide range of applications, including art generation, content creation, natural language processing, and more. Whether you're a data scientist, AI enthusiast, or industry professional, this book serves as an invaluable resource for mastering the art and science of generative AI. A Commitment to Responsible AI As we explore the boundless potential of Generative AI, it is imperative to acknowledge and address the ethical and societal impacts of these technologies. The "GenAI Black Book" is committed to fostering a responsible and ethical approach to AI development and deployment, ensuring that these powerful tools are used to benefit society at large. For the Reader This book is designed to be accessible to a wide audience. Each chapter builds upon the previous ones, making it suitable for both self-study and academic use. Code examples are provided in Python, leveraging popular libraries such as Hugging Face's Transformers, to offer hands-on experience with implementing GenAI systems. We hope that the "GenAI Black Book" will inspire, educate, and empower you to explore the exciting possibilities of Generative AI. Whether you aim to develop cutting-edge applications, contribute to research, or simply satisfy your curiosity, this book is your gateway to understanding and mastering Generative AI. Welcome to the future of creativity and innovation. Welcome to the "GenAI Black Book".

Building Intelligent Applications with Generative AI

Author : Yattish Ramhorry
Publisher : BPB Publications
ISBN 13 : 9355519133
Total Pages : 333 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Building Intelligent Applications with Generative AI by : Yattish Ramhorry

Download or read book Building Intelligent Applications with Generative AI written by Yattish Ramhorry and published by BPB Publications. This book was released on 2024-08-22 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION Building Intelligent Applications with Generative AI is a comprehensive guide that unlocks the power of generative AI for building cutting-edge applications. This book covers a wide range of use cases and practical examples, from text generation and conversational agents to creative media generation and code completion. These examples are designed to help you capitalize on the potential of generative AI in your applications. Through clear explanations, step-by-step tutorials, and real-world case studies, you will learn how to prepare data and train generative AI models. You will also explore different generative AI techniques, including large language models like GPT-4, ChatGPT, Llama 2, and Google’s Gemini, to understand how they can be applied in various domains, such as content generation, virtual assistants, and code generation. With a focus on practical implementation, this book also examines ethical considerations, best practices, and future trends in generative AI. Further, this book concludes by exploring ethical considerations and best practices for building responsible GAI applications, ensuring you are harnessing this technology for good. By the end of this book, you will be well-equipped to leverage the power of GAI to build intelligent applications and unleash your creativity in innovative ways. KEY FEATURES ● Learn the fundamentals of generative AI and the practical usage of prompt engineering. ● Gain hands-on experience in building generative AI applications. ● Learn to use tools like LangChain, LangSmith, and FlowiseAI to create intelligent applications and AI chatbots. WHAT YOU WILL LEARN ● Understand generative AI (GAI) and large language models (LLMs). ● Explore real-world GAI applications across industries. ● Build intelligent applications with the ChatGPT API. ● Explore retrieval augmented generation with LangChain and Gemini Pro. ● Create chatbots with LangChain and Streamlit for data retrieval. WHO THIS BOOK IS FOR This book is for developers, data scientists, AI practitioners, and tech enthusiasts who are interested in leveraging generative AI techniques to build intelligent applications across various domains. TABLE OF CONTENTS 1. Exploring the World of Generative AI 2. Use Cases for Generative AI Applications 3. Mastering the Art of Prompt Engineering 4. Integrating Generative AI Models into Applications 5. Emerging Trends and the Future of Generative AI 6. Building Intelligent Applications with the ChatGPT API 7. Retrieval Augmented Generation with Gemini Pro 8. Generative AI Applications with Gradio 9. Visualize your Data with LangChain and Streamlit 10. Building LLM Applications with Llama 2 11. Building an AI Document Chatbot with Flowise AI 12. Best Practices for Building Applications with Generative AI 13. Ethical Considerations of Generative AI