This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.
Did Krishna fail in his negotiation skills? Could the war have been avoided? As the two women stood in the court of King Solomon of Israel, both claiming to be the mother of the child, what strategy did the monarch follow to crack the case? Though he couldn't reach the water which was at the bottom of the pitcher, how did the crow in Aesop's fables finally quench his thirst? How did the Zen master make the businessman realise the virtues of having an open mind? When Buddha was insulted by the angry young man, how did he react to the provocation? In a world that is riddled with uncertainties and challenges, just knowing your job may not be enough. It is one thing to read management books and quite another to stay inspired and be on top of your game every day. How do you communicate at work? How do you show empathy? How do you effectively network and build lasting relationships? How can a conflict situation be managed? Can you master the art of getting along with people? Ancient Secrets of Soft Skills Unravelled teaches you all that and brings you stories from the Mahabharata, the teachings of Zen and Buddha, the wisdom of King Solomon, the survival tactics learnt from the Aesop's fables and the author's lived experiences too in an attempt to present soft skills as an essential tool to life skills. Anecdotal and relatable, it brings alive a range of skills and strategies dating back to centuries that are relevant even today, underlining the efficacy of soft skills and the need to acquire it early on in life.
Which Kaurava was inspired by the birds to commit one of the most grotesque murders in the Mahabharata? Why did King Muchkund sleep for a million years and wake up in the Dwaparyug? Whose soul had entered the dice of Shakuni? Why was Gandhari married to a goat before she wed Dhritarashtra? What was the secret behind Arjun's chariot being burnt to ashes after the war? Who instigated Janmejay to burn every snake in the universe? Who was the only Kaurava to cross over to the Pandavas before the battle? An epic that never dies and still remains relevant even thousands of years later-Vyasa's Mahabharata has always captured our imagination. The saga of two feuding families, the Mahabharata, with its various twists and turns, has been a compelling read across generations, inspiring many to dig deeper into the great poem. This collection of twenty short stories brings out characters and incidents that are largely unheard of and are buried in the vastness of the epic. Capturing every emotion from valour, lust, loyalty and treachery to goodness and ethics so relevant to the world we live in, these stories help us understand the epic better by bringing out a different dimension altogether.
Influential personalities of each person in the crowd because of its priceless heritage which has made her identity. Family or society, country or abroad, job or industry, business administration or political system, for instance Everywhere you look a lot people will stand among them who could most influence and dignity of all to find what would be your ideal. You have to be the same. This book to make you so attractive and influential gives sound advice. #v&spublishers
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group
Industrial assets (such as railway lines, roads, pipelines) are usually huge, span long distances, and can be divided into clusters or segments that provide different levels of functionality subject to different loads, degradations and environmental conditions, and their efficient management is necessary. The aim of the book is to give comprehensive understanding about the use of autonomous vehicles (context of robotics) for the utilization of inspection and maintenance activities in industrial asset management in different accessibility and hazard levels. The usability of deploying inspection vehicles in an autonomous manner is explained with the emphasis on integrating the total process. Key Features Aims for solutions for maintenance and inspection problems provided by robotics, drones, unmanned air vehicles and unmanned ground vehicles Discusses integration of autonomous vehicles for inspection and maintenance of industrial assets Covers the industrial approach to inspection needs and presents what is needed from the infrastructure end Presents the requirements for robot designers to design an autonomous inspection and maintenance system Includes practical case studies from industries
Data flow analysis is used to discover information for a wide variety of useful applications, ranging from compiler optimizations to software engineering and verification. Modern compilers apply it to produce performance-maximizing code, and software engineers use it to re-engineer or reverse engineer programs and verify the integrity of their programs. Supplementary Online Materials to Strengthen Understanding Unlike most comparable books, many of which are limited to bit vector frameworks and classical constant propagation, Data Flow Analysis: Theory and Practice offers comprehensive coverage of both classical and contemporary data flow analysis. It prepares foundations useful for both researchers and students in the field by standardizing and unifying various existing research, concepts, and notations. It also presents mathematical foundations of data flow analysis and includes study of data flow analysis implantation through use of the GNU Compiler Collection (GCC). Divided into three parts, this unique text combines discussions of inter- and intraprocedural analysis and then describes implementation of a generic data flow analyzer (gdfa) for bit vector frameworks in GCC. Through the inclusion of case studies and examples to reinforce material, this text equips readers with a combination of mutually supportive theory and practice, and they will be able to access the author’s accompanying Web page. Here they can experiment with the analyses described in the book, and can make use of updated features, including: Slides used in the authors’ courses The source of the generic data flow analyzer (gdfa) An errata that features errors as they are discovered Additional updated relevant material discovered in the course of research
We take liberalism to be a set of ideas committed to political rights and self-determination, yet it also served to justify an empire built on political domination. Uday Mehta argues that imperialism, far from contradicting liberal tenets, in fact stemmed from liberal assumptions about reason and historical progress. Confronted with unfamiliar cultures such as India, British liberals could only see them as backward or infantile. In this, liberals manifested a narrow conception of human experience and ways of being in the world. Ironically, it is in the conservative Edmund Burke—a severe critic of Britain's arrogant, paternalistic colonial expansion—that Mehta finds an alternative and more capacious liberal vision. Shedding light on a fundamental tension in liberal theory, Liberalism and Empire reaches beyond post-colonial studies to revise our conception of the grand liberal tradition and the conception of experience with which it is associated.
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.
Professionals as well as researchers can benefit from this comprehensive introduction into the topic of setup planning, which reflects the latest state of research and gives hands-on examples. Starting with a brief but thorough introduction, this book explains the significance of setup planning in process planning and includes a reflection on its external constraints. Step-by-step the different phases of setup planning are outlined and traditional as well as modern approaches, such as fuzzy logic based setup planning, on the solution of setup planning problems are presented. Three detailed examples of applications provide a clear and accessible insight into the up-to-date techniques and various approaches in setup planning.
Ultimately, what really does it mean to be creative? How can we see ourselves as participating in the creativity of God for mission? All people are creative. Sadly, however, for many, creativity is stifled and remains stunted due to several reasons--social, economic, political, cultural, and even spiritual. This study explores how ICMs--indigenous cosmopolitan musicians--negotiate their creativity amid the liminal spaces they occupy as they share in the creativity of God for mission through their music. But what exactly does it mean to share in the creativity of God for mission? Contrary to popular notion, ICMs evidence that creativity is not merely innovation; it is not a psychological metric for measuring human potential; it is certainly not the "icing on the cake" reserved for a few so-called creatives or artists. Rather, "theological creativity" is participation in the creatio Dei; it is theologically prior to mission. As a missiological framework, creatio Dei is understood here in terms of creative being, creative construction (design), and creative performance. Hopefully, this book can help clarify and expand our understanding of what it means to be truly creative and, thereby, with the help of the Creator, put into practice principles of theological creativity as we share in the creativity of God in the world, with others.
This invaluable book provides an elementary description of supersymmetric quantum mechanics which complements the traditional coverage found in the existing quantum mechanics textbooks. It gives physicists a fresh outlook and new ways of handling quantum-mechanical problems, and also leads to improved approximation techniques for dealing with potentials of interest in all branches of physics. The algebraic approach to obtaining eigenstates is elegant and important, and all physicists should become familiar with this. The book has been written in such a way that it can be easily appreciated by students in advanced undergraduate quantum mechanics courses. Problems have been given at the end of each chapter, along with complete solutions to all the problems. The text also includes material of interest in current research not usually discussed in traditional courses on quantum mechanics, such as the connection between exact solutions to classical solution problems and isospectral quantum Hamiltonians, and the relation to the inverse scattering problem.
Contributed articles on foreign relations of India post 1984 and national security concerns presented earlier at a seminar celebrating 40th anniversary of Institute for Defence Studies and Analyses.
This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.
Will Modi pass the litmus test of governance? Will his performance match his promises? In Marching with a Billion, Uday Mahurkar analyses the Modi government's three years in power against the backdrop of years of policy paralysis and corruption before he came to power, leaving him with a stiff salvaging job. Focusing on key areas of governance like infrastructure, foreign affairs, power, the social sector, finance, digital technology and agriculture, the author showcases the work of the present government and the monumental changes the prime minister has brought about, including digital innovation and the uprooting of middlemen, which has resulted in an unprecedented level of transparency, and a resolute assault on poverty. He also points out some of the shortfalls of Modi's government, subjecting it to critical evaluation. Will Modi become a great institution builder, a prerequisite to becoming a nation builder? What will be the long-term impact of demonetization on the economy? This book has the answers.
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