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
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside Recursion, memoization, and bit manipulation Search, graph, and genetic algorithms Constraint-satisfaction problems K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
The emergence of Generative AI has marked a significant turning point, heralding a new age of innovation and intellectual exploration. Much like a compelling narrative, this advancement in artificial intelligence has captivated the global community and ushered in an unprecedented surge of innovation. For many years, the subtle hum of AI has been interconnected into the fabric of our society. Devices such as Echo (Alexa) and Google Home, once considered avant-garde, are now seamlessly integrated into our homes and vehicles, becoming essential navigators in our daily journeys. However, this new phase of AI evolution is distinct. Capable of enhanced ability, these new generative AI systems could easily discern the intuitive needs of their end-users. With a mere command or a simple image, generative AI systems can draft comprehensive reports, write legal documents, or produce intricate visual masterpieces. Their proficiency can also be extended to routine and mundane tasks, smoothly managing administrative duties, writing correspondences, and providing invaluable support in professional settings. Although these foundation models require a vast amount of training data and billions of parameters to be effective, the outcomes are equally remarkable. Technology companies and investors, recognising the potential, embarked on an investment spree; steering a new era for the development of specialised models such as Microsoft CoPilot, Midjourney, ChatGPT, and so on. As these technology companies continue to improve their language models, each version seems to be more refined than its predecessor. Foremost among these are the Large Language Models (LLMs), emblematic of this AI renaissance. Now, with voice-activated capabilities, generative AIs have become capable of much more in the relatively short span of their existence. With the integration of voice-activated features, combined with their capabilities to speak like a human being, their potential continues to grow exponentially. They aren’t labelled as ‘chatbots’ anymore – these AI systems signify a technological paradigm shift, reshaping humanity’s understanding of technology, automation, and creative expression. However, with such shift comes the imperative need for governance and control. The unchecked expansion of AI poses unmitigated challenges. In reality, these advanced AIs have the potential to be transformative and destructive parallelly. Thus, it is necessary to establish guidelines and oversight to ensure the ethical deployment of such systems which this book focuses on. As society stands at this transformative crossroads, parallels are being drawn to the imaginative world of fiction. The concept of ‘Jarvis’ AI from the fictitious world appears provocatively close to becoming a reality. The epoch of generative AI has truly dawned, promising a future where technological prowess and human aspiration unite.
AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.
"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
You belong in this world of computer science education—and because of you, adults of the future will understand how to responsibly participate in high-tech environments with confidence. Districts, cities, and states are moving toward computer science requirements for all K-12 classrooms, even in courses that were not previously associated with technology. These new requirements leave many teachers feeling anxious and unprepared when it comes to integrating computer science into existing curriculum. This book is here to support educators in that shift by inviting them to explore computer science and coding in an approachable and unintimidating way. Let′s All Teach Computer Science: K-12 is a source of inspiration and empowerment for educators who are moving into this technological wonderland. Kiki Prottsman has more than 15 years of experience in computer science education, and her insight informs thoughtful discussions on promoting creativity, problem-solving, and collaboration in students. The book positions computer science in a way that supports other essential skills–such as reading, writing, and mathematics– by providing customizable frameworks that help to seamlessly integrate computer science into core subjects. This book: Provides powerful insights for creating innovative and inclusive learning environments Offers practical examples of integrating computer science into traditional subjects like math, history, art, and more Highlights the importance of addressing implicit biases and promoting computer science as an inclusive field for all students Includes insights on classroom technology and educational technology, as well as AI and its role in education Encourages educators to work together to nurture digital innovators while recognizing potential challenges and frustrations Let′s All Teach Computer Science is an essential guide that equips K-12 teachers with the knowledge and tools necessary to begin teaching computer science immediately–and does so in an enjoyable way, thanks to Prottsman’s friendly and playful style.
