Download or read book AI Foundations of Generative AI written by Jon Adams and published by Green Mountain Computing. This book was released on with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the captivating world of Generative AI with "AI Foundations of Generative AI," a groundbreaking exploration at the crossroads of creativity and technology. This enlightening book serves as your comprehensive guide through the burgeoning field of Generative AI, where machines wield the power of creativity to produce art, music, literature, and more. Authored by leading experts in the field, this book demystifies the complex algorithms behind AI's ability to emulate human creativity, offering readers a front-row seat to the future of digital innovation. Key Features: Engaging Content: Written in an accessible style, free from daunting jargon, making complex concepts approachable for all readers. Interactive Exercises: Hands-on activities to deepen understanding of AI principles. Ethical Considerations: Insightful discussions on the moral implications of virtual influencers, deepfakes, and AI-driven creativity. Chapters Overview: The Digital Composer: Uncover how AI creates symphonies that challenge the works of great composers. Artistic Algorithms: Explore the systems generating visual art indistinguishable from human-created pieces. Wordsmiths of the Digital Age: Delve into how AI crafts poetry and prose with the finesse of human writers. Synthesized Realities: Navigate the creation of hyper-realistic images, videos, and sounds through AI. Virtual Influencers and Moral Codes: Examine the ethical dimensions of AI-driven personalities and content. Data Driven Storytelling: Understand how AI transforms data into compelling narratives and interactive experiences. Chat GPT and Open AI: Gain insights into the organizations and technologies at the forefront of generative AI. Content Tailored by Technology: Discover the future of personalized media and digital environments shaped by AI. Perfect for tech enthusiasts, creative professionals, and anyone curious about the intersection of art and artificial intelligence, "AI Foundations of Generative AI" offers a unique lens through which to view the future of creativity and technology. Whether you're a tech-savvy reader or new to the world of AI, this book promises to enlighten and inspire with its vision of a world where creativity knows no bounds. Embark on a Journey of Discovery: Prepare to be both enlightened and inspired as you explore the limitless possibilities of Generative AI. "AI Foundations of Generative AI" is your ticket to understanding and participating in the future of creative technology.

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Download or read book Generative AI Foundations in Python written by Carlos Rodriguez and published by Packt Publishing Ltd. This book was released on 2024-07-26 with total page 190 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 DescriptionThe 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.

Download or read book Artificial Intelligence written by David L. Poole and published by Cambridge University Press. This book was released on 2017-09-25 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Download or read book Responsible Artificial Intelligence written by Virginia Dignum and published by Springer Nature. This book was released on 2019-11-04 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.

Download or read book Foundations of Distributed Artificial Intelligence written by G. M. P. O'Hare and published by John Wiley & Sons. This book was released on 1996-04-05 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed Artificial Intelligence (DAI) is a dynamic area of research and this book is the first comprehensive, truly integrated exposition of the discipline presenting influential contributions from leaders in the field. Commences with a solid introduction to the theoretical and practical issues of DAI, followed by a discussion of the core research topics--communication, coordination, planning--and how they are related to each other. The third section describes a number of DAI testbeds, illustrating particular strategies commissioned to provide software environments for building and experimenting with DAI systems. The final segment contains contributions which consider DAI from different perspectives.

Download or read book Generative Artificial Intelligence written by Shivam R Solanki and published by Apress. This book was released on 2024-07-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today’s AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will Learn What is Generative Artificial Intelligence? What are text-to-image synthesis techniques and conditional image generation? What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques? What are text-to-video models and how do you tune them? What are large language models, and how do you tune them? Who This Book Is For Those with intermediate to advanced technical knowledge in artificial intelligence and machine learning

Download or read book AI Foundations of GPT written by Jon Adams and published by Green Mountain Computing. This book was released on with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the heart of artificial intelligence with "AI Foundations of GPT," a groundbreaking book that charts the journey of Generative Pre-trained Transformers (GPT) from their conceptual inception to their role as cornerstones of modern AI applications. This meticulously crafted text serves as both a historical narrative and a forward-looking discussion, exploring the myriad ways in which GPT technology is reshaping our digital landscape. Key Features: Comprehensive Coverage: From the AI revolution to the future of GPT, each chapter is dedicated to a different facet of GPT technology, ensuring readers gain a well-rounded understanding of its complexities and capabilities. Accessible Explanations: Designed to cater to both AI aficionados and newcomers, the book explains the technical underpinnings of GPT models in an engaging and understandable manner. Future-Oriented: Offers a peek into the potential advancements and challenges that lie ahead for GPT technology, encouraging readers to ponder its implications for society and industry. Chapters: The AI Revolution: An overview of how artificial intelligence has evolved, setting the stage for the emergence of GPT. Understanding GPT: Breaks down the basics of Generative Pre-trained Transformers, explaining what they are and why they matter. The Mechanics of GPT: Delves into the technical aspects of how GPT models work, from algorithms to neural networks. Training GPT Models: Discusses the process of training GPT models, highlighting the resources and methodologies involved. Applications of GPT: Explores the diverse applications of GPT in various fields such as literature, customer service, and software development. Ethical Considerations: Examines the ethical dilemmas and considerations surrounding the use of GPT technology. The Business of GPT: Analyzes the economic landscape of GPT, including its impact on industries and business models. Limitations and Challenges: Acknowledges the limitations of current GPT models and the challenges facing their development. The Future of GPT: Speculates on the future advancements of GPT technology and its potential societal impacts. Whether you're deeply embedded in the world of AI or simply curious about the technologies shaping our future, "AI Foundations of GPT" offers a rich, insightful exploration of one of the most significant developments in artificial intelligence. Embark on this journey to understand not just the mechanics of GPT, but its profound implications on our world.

Download or read book Generative AI Research written by Anand Vemula and published by Independently Published. This book was released on 2024-06-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI Research: Mastering Foundations, Models, and Practical Applications is a comprehensive guide that delves into the fascinating world of generative artificial intelligence. This book is meticulously designed for researchers, practitioners, and enthusiasts who are keen to explore and harness the power of generative AI. Starting with an introduction to AI and machine learning, the book provides a solid foundation by explaining key concepts and the historical development of generative models. It dives into the mathematical and statistical underpinnings essential for understanding generative AI, followed by a thorough exploration of machine learning and deep learning fundamentals. The book categorizes and examines various types of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), autoregressive models, and flow-based models. Each section covers the architecture, applications, and challenges associated with these models, supplemented with real-world examples and use cases. Readers will find detailed tutorials with complete solutions, enabling hands-on learning and practical implementation of concepts. For instance, the section on GANs provides step-by-step guidance on building and training GANs, addressing common pitfalls and optimization strategies. Moreover, the book highlights diverse applications of generative AI across various domains such as image generation, text creation, music synthesis, and video editing. Advanced topics like conditional generative models, multimodal generative models, and few-shot learning are also discussed, offering insights into cutting-edge research and developments. Practical exercises with complete solutions are included to reinforce learning and provide a robust understanding of how to apply generative AI techniques in real-world scenarios. The book also addresses the evaluation metrics for generative models, ensuring readers can effectively measure the performance of their models. Generative AI Research: Mastering Foundations, Models, and Practical Applications is an essential resource that bridges the gap between theory and practice, equipping readers with the knowledge and skills needed to excel in the dynamic field of generative AI.

Download or read book AI Foundations of Artificial General Intelligence written by Jon Adams and published by Green Mountain Computing. This book was released on with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the pioneering realm of Artificial General Intelligence with Jon Adams'sAI Foundations of Artificial General Intelligence (AGI), the first installment of the enlightening Foundations Of series. This groundbreaking book serves as a comprehensive guide to the evolving field of AI, offering a deep dive into the quest for Artificial General Intelligence (AGI) – a frontier of technology aiming to create machines that rival human intelligence in their versatility and creativity. Structured in a reader-friendly format, this book covers an impressive array of topics: The Genesis of AI: Explore the origins and evolution of artificial intelligence, setting the stage for a journey towards AGI. Understanding Machine Learning: Get to grips with the core concepts and techniques that fuel the growth of AI. Neural Networks and Deep Learning: Delve into the architectures that mimic the human brain's functionality. Cognitive Architectures – A Blueprint for AGI: Understand the frameworks designed to support the development of AGI. Narrow AI vs General AI: Learn about the differences between AI for specific tasks and the envisioned AGI capable of human-like reasoning. Defining Human-Like Intelligence: Investigate what it means for a machine to possess intelligence indistinguishable from humans. Computer Science Meets Neuroscience: Discover the intersection between computational models and the complexities of the human brain. Challenges in Replicating Human Thought: Examine the hurdles in simulating human cognitive processes. Toward the Future of AGI: Speculate on the potential directions and implications of achieving AGI. AGI and Human Identity – Redefining the Species: Reflect on how AGI could transform our understanding of human identity. Preempting Skynet – Safeguarding Against AGI Risks: Address the ethical considerations and safety measures in the development of AGI. AI Foundations of AGI is meticulously crafted for both novices and seasoned enthusiasts of artificial intelligence. Adams simplifies complex concepts without sacrificing depth, making advanced topics in AI and AGI accessible to all. This book not only sheds light on the technical aspects of AI development but also encourages readers to ponder the philosophical implications of creating machines with human-like intelligence. Whether you're a student, professional, or simply curious about the future of AI, this book offers valuable insights into the ambitious goal of achieving AGI. Join Jon Adams on a captivating exploration of AI's potential to redefine our world, armed with the knowledge and foresight to navigate the challenges and opportunities that lie ahead.

Download or read book Generative AI Networks written by Anand Vemula and published by Independently Published. This book was released on 2024-06-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Generative AI Networks: Foundations, Models, Applications, and Future Directions" is a comprehensive guide that delves into the world of generative artificial intelligence (AI). This book begins by establishing the fundamental principles of generative AI, exploring its historical evolution, mathematical foundations in probability theory and neural networks, and deep learning fundamentals essential for understanding advanced generative models. Moving into the core of the book, readers are introduced to various generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models. Each model is explained in detail, covering their architectures, training techniques, and practical applications across domains like image generation, text synthesis, and audio composition. Real-world use cases and case studies illustrate how these models are transforming industries such as healthcare, entertainment, and finance. The book then advances into more sophisticated generative models including Flow-based Models and Diffusion Models, offering insights into their training methodologies and applications. Hybrid and multi-modal generative models are explored, demonstrating how these integrated approaches enhance the capability of AI systems to generate complex and diverse outputs. Practical considerations and ethical implications of generative AI are thoroughly discussed, emphasizing topics like bias mitigation, fairness, and regulatory considerations. The final chapters explore emerging trends and future directions in generative AI, highlighting ongoing research, challenges, and opportunities for innovation.

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.

Download or read book Artificial Intelligence Fundamentals for Business Leaders written by I. Almeida and published by Now Next Later AI. This book was released on 2023-06 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: 2024 Edition. Free access to the AI Academy! The perfect guide to help non-technical business leaders understand the power of AI. Completely up to date with the latest advancements in generative AI. Part of the Byte-sized Learning AI series by Now Next Later AI, these books break down complex concepts into easily digestible pieces, providing you with a solid foundation in the fundamentals of AI. More Than a Book By purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. You will also receive free modules and 50% discount toward the enrollment in the self-paced course of the same name and enjoy video summary lessons, instructor-graded assignments, and live sessions. A course certificate will be awarded upon successful completion. AI Academy by Now Next Later AI We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically. Book and Course Learning Rubric - Chapters 1-7: Understanding of AI [11%] —Demonstrated comprehension of AI's evolution, definition, applications, and comparison with human intelligence. - Chapters 8-13: Understanding of Data and Data Management [11%] — Clear understanding of the significance of big data, and strategies for data management. - Chapters 14-29: Understanding of Machine Learning [30%] — Familiarity with machine learning algorithms, different learning types, and the key steps involved in a machine learning project. - Chapters 30-35: Understanding of Deep Learning [9%] — Understanding of deep learning, its basics, and the structure and types of neural networks. - Chapters 36-40: Understanding of Model Selection and Evaluation [9%] — Ability to select and evaluate machine learning models and utilize them for decision-making. - Chapters 41-50: Understanding of Generative AI [15%] — Detailed understanding of generative AI, its value chain, models, prompt strategies, applications, opportunities, and governance challenges. Assignment: Practical Application [15%] — Ability to apply generative AI understanding to real-world business challenges, demonstrating critical thinking and strategic planning skills.

Download or read book Artificial Intelligence Fundamentals for Business Leaders written by I. Almeida and published by Byte-sized Learning. This book was released on 2023-12-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The perfect guide to help non-technical business leaders understand the power of AI. Completely up to date with the latest advancements in generative AI. This series delivers a solid foundation in the fundamentals of AI.

Download or read book AI Mastery Trilogy written by Andrew Hinton and published by Book Bound Studios. This book was released on with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the "AI Mastery Trilogy," the ultimate collection for professionals seeking to conquer the world of artificial intelligence (AI). This 3-in-1 compendium is meticulously crafted to guide you from the foundational principles of AI to the intricate mathematical frameworks and practical coding applications that will catapult your expertise to new heights. Book 1: "AI Basics for Managers" by Andrew Hinton is your gateway to understanding and implementing AI in business. It equips managers with the knowledge to navigate the AI landscape, identify opportunities, and lead their organizations toward a future of innovation and growth. Book 2: "Essential Math for AI" demystifies the mathematical backbone of AI, offering a deep dive into the core concepts that fuel AI systems. From linear algebra to game theory, this book is a treasure trove for anyone eager to grasp the numerical and logical foundations that underpin AI's transformative power. Book 3: "AI and ML for Coders" is the hands-on manual for coders ready to harness AI and machine learning in their projects. It provides a comprehensive overview of AI and ML technologies, practical coding advice, and ethical considerations, ensuring you're well-equipped to create cutting-edge, responsible AI applications. The "AI Mastery Trilogy" is more than just a set of books; it's a comprehensive learning journey designed to empower business leaders, mathematicians, and coders alike. Whether you're looking to lead, understand, or build the future of AI, this collection is an indispensable resource for mastering the art and science of one of the most exciting fields in technology. Embrace the AI revolution and secure your copy of the "AI Mastery Trilogy" today!

Download or read book TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

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