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Delve into the fascinating world of machine learning with this comprehensive guide, which unpacks the algorithms driving today's intelligent systems. From foundational concepts to advanced applications, this book is essential for anyone looking to understand the mechanics behind AI.
Book Synopsis Algorithms of Intelligence: Exploring the World of Machine Learning by : Dr R. Keerthika
Download or read book Algorithms of Intelligence: Exploring the World of Machine Learning written by Dr R. Keerthika and published by Inkbound Publishers. This book was released on 2022-01-20 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into the fascinating world of machine learning with this comprehensive guide, which unpacks the algorithms driving today's intelligent systems. From foundational concepts to advanced applications, this book is essential for anyone looking to understand the mechanics behind AI.
Machine learning is a field of Artificial intelligence that provides algorithms those can learn and improve from experiences. Machine learning algorithms are turned as integral parts of today’s digital life. Its applications include recommender systems, targeted campaigns, text categorization, computer vision and auto security systems etc. Machine learning also considered as essential part of data science due to its capability of providing predictive analytics, capability in handling variety of data and suitability for big data applications. Its capability for predictive analytics resulted of its general structure that is building statistical models out of training data. In other hand easy scalability advantage of machine learning algorithms is making them to be suitable for big data applications. The different types of learning algorithms includes supervised learning, unsupervised learning, reinforcement learning, feature learning, rule based learning, Robot or expert system learning, sparse dictionary and anomaly detection. These learning algorithms can be realized by computing models artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, Genetic algorithms and soft computing. The familiar tools to implement machine learning algorithms include Python, R, Matlab, Scala, Clojure and Ruby. Involving of such open source programming languages, tools and social network communities makes the machine learning most progressing filed of computer science. The machine learning life cycle includes defining project objectives, explore the types and format, modeling data to fit for machine learning algorithms, deciding suitable machine learning model and implement and decide best result from data for decision making. These days, machine learning is observing great interest by the society and it has turned as one of the significant responsibility of top level managers to transform their business in the profitable means by exploring its basic functionalities. The world is at the sheer of realizing a situation where machines will work in agreement with human being to work together, operation, and advertise their services in a novel way which is targeted, valuable, and well-versed. In order to achieve this, they can influence machine learning distinctiveness. Dr. Raghuram Bhukya
Book Synopsis Exploring Machine Learning: A Beginners Perspective by : Dr. Raghuram Bhukya
Download or read book Exploring Machine Learning: A Beginners Perspective written by Dr. Raghuram Bhukya and published by Horizon Books ( A Division of Ignited Minds Edutech P Ltd). This book was released on 2021-04-20 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a field of Artificial intelligence that provides algorithms those can learn and improve from experiences. Machine learning algorithms are turned as integral parts of today’s digital life. Its applications include recommender systems, targeted campaigns, text categorization, computer vision and auto security systems etc. Machine learning also considered as essential part of data science due to its capability of providing predictive analytics, capability in handling variety of data and suitability for big data applications. Its capability for predictive analytics resulted of its general structure that is building statistical models out of training data. In other hand easy scalability advantage of machine learning algorithms is making them to be suitable for big data applications. The different types of learning algorithms includes supervised learning, unsupervised learning, reinforcement learning, feature learning, rule based learning, Robot or expert system learning, sparse dictionary and anomaly detection. These learning algorithms can be realized by computing models artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, Genetic algorithms and soft computing. The familiar tools to implement machine learning algorithms include Python, R, Matlab, Scala, Clojure and Ruby. Involving of such open source programming languages, tools and social network communities makes the machine learning most progressing filed of computer science. The machine learning life cycle includes defining project objectives, explore the types and format, modeling data to fit for machine learning algorithms, deciding suitable machine learning model and implement and decide best result from data for decision making. These days, machine learning is observing great interest by the society and it has turned as one of the significant responsibility of top level managers to transform their business in the profitable means by exploring its basic functionalities. The world is at the sheer of realizing a situation where machines will work in agreement with human being to work together, operation, and advertise their services in a novel way which is targeted, valuable, and well-versed. In order to achieve this, they can influence machine learning distinctiveness. Dr. Raghuram Bhukya
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
Book Synopsis Genetic Algorithms and Machine Learning for Programmers by : Frances Buontempo
Download or read book Genetic Algorithms and Machine Learning for Programmers written by Frances Buontempo and published by Pragmatic Bookshelf. This book was released on 2019-01-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
"The Power of Artificial Intelligence: Exploring Machine Learning and Neural Networks" is a book that provides an overview of the field of artificial intelligence (AI), with a focus on machine learning and neural networks. The book explains the basic concepts and techniques of AI and how they can be applied to solve real-world problems. The book covers a wide range of topics, including supervised and unsupervised learning, neural network architectures, deep learning, natural language processing, computer vision, and more. It also discusses the ethical and societal implications of AI, as well as the future of the field. The book is written in a clear and concise style, making it accessible to readers with a basic understanding of computer science and mathematics. It is aimed at anyone interested in learning about AI and its potential applications, including students, researchers, and professionals in fields such as data science, engineering, and computer programming.
Book Synopsis The Power of Artificial Intelligence Exploring Machine Learning and Neural Networks by :
Download or read book The Power of Artificial Intelligence Exploring Machine Learning and Neural Networks written by and published by Sunil Kumar Saini. This book was released on 2023-04-27 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The Power of Artificial Intelligence: Exploring Machine Learning and Neural Networks" is a book that provides an overview of the field of artificial intelligence (AI), with a focus on machine learning and neural networks. The book explains the basic concepts and techniques of AI and how they can be applied to solve real-world problems. The book covers a wide range of topics, including supervised and unsupervised learning, neural network architectures, deep learning, natural language processing, computer vision, and more. It also discusses the ethical and societal implications of AI, as well as the future of the field. The book is written in a clear and concise style, making it accessible to readers with a basic understanding of computer science and mathematics. It is aimed at anyone interested in learning about AI and its potential applications, including students, researchers, and professionals in fields such as data science, engineering, and computer programming.
A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.
Book Synopsis A Human's Guide to Machine Intelligence by : Kartik Hosanagar
Download or read book A Human's Guide to Machine Intelligence written by Kartik Hosanagar and published by Penguin. This book was released on 2020-03-10 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.
Step into a world where machines think, learn, and reshape our very existence. "Artificial Intelligence (AI) Unleashed: Exploring The Boundless Potential Of AI" is a captivating exploration of the transformative power of AI. From healthcare to finance, transportation to education, this book unravels the mysteries of AI, demystifying complex algorithms and showcasing real-world applications. Discover how AI is revolutionizing industries, empowering communities, and challenging our understanding of intelligence. Delve into the ethical considerations and responsible development of AI as we navigate the uncharted territories of bias, transparency, and accountability. With well researched information, this book is your guide to understanding and embracing the boundless potential of artificial intelligence. Get ready to embark on a journey that will reshape your perception of what is possible in the age of AI.
Book Synopsis Artificial Intelligence (AI) Unleashed by : Michael McNaught
Download or read book Artificial Intelligence (AI) Unleashed written by Michael McNaught and published by Michael McNaught. This book was released on 2023-06-10 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step into a world where machines think, learn, and reshape our very existence. "Artificial Intelligence (AI) Unleashed: Exploring The Boundless Potential Of AI" is a captivating exploration of the transformative power of AI. From healthcare to finance, transportation to education, this book unravels the mysteries of AI, demystifying complex algorithms and showcasing real-world applications. Discover how AI is revolutionizing industries, empowering communities, and challenging our understanding of intelligence. Delve into the ethical considerations and responsible development of AI as we navigate the uncharted territories of bias, transparency, and accountability. With well researched information, this book is your guide to understanding and embracing the boundless potential of artificial intelligence. Get ready to embark on a journey that will reshape your perception of what is possible in the age of AI.
In this book, we explore the fascinating world of artificial intelligence, from its inception to its present-day applications and potential future implications. By examining the fundamental concepts, algorithms, and techniques, we aim to demystify AI and provide readers with a comprehensive understanding of this rapidly evolving field. We also delve into the ethical and societal considerations surrounding AI, ensuring that readers grasp both the promises and challenges associated with its implementation. Whether you are a novice curious about AI or a seasoned professional seeking deeper insights, this book will serve as a valuable resource, shedding light on the rise of machines and their impact on our world. Chapters included: Chapter 1: Introduction to Artificial Intelligence Chapter 2: The Fundamentals of AI Chapter 3: Machine Learning Algorithms Chapter 4: Deep Learning Chapter 5: Natural Language Processing Chapter 6: Robotics and AI Chapter 7: AI and Society Chapter 8: Future of AI Chapter 9: Ethical and Legal Implications Chapter 10: AI and Human Collaboration Chapter 11: The Philosophy of AI Chapter 12: Conclusion
Book Synopsis Rise of the Machines by : Christopher Ford
Download or read book Rise of the Machines written by Christopher Ford and published by IT Collection. This book was released on 2023-07-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we explore the fascinating world of artificial intelligence, from its inception to its present-day applications and potential future implications. By examining the fundamental concepts, algorithms, and techniques, we aim to demystify AI and provide readers with a comprehensive understanding of this rapidly evolving field. We also delve into the ethical and societal considerations surrounding AI, ensuring that readers grasp both the promises and challenges associated with its implementation. Whether you are a novice curious about AI or a seasoned professional seeking deeper insights, this book will serve as a valuable resource, shedding light on the rise of machines and their impact on our world. Chapters included: Chapter 1: Introduction to Artificial Intelligence Chapter 2: The Fundamentals of AI Chapter 3: Machine Learning Algorithms Chapter 4: Deep Learning Chapter 5: Natural Language Processing Chapter 6: Robotics and AI Chapter 7: AI and Society Chapter 8: Future of AI Chapter 9: Ethical and Legal Implications Chapter 10: AI and Human Collaboration Chapter 11: The Philosophy of AI Chapter 12: Conclusion
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
Book Synopsis Machine Learning and AI in Finance by : German Creamer
Download or read book Machine Learning and AI in Finance written by German Creamer and published by Routledge. This book was released on 2021-04-05 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Book Synopsis Practical Deep Learning for Cloud, Mobile, and Edge by : Anirudh Koul
Download or read book Practical Deep Learning for Cloud, Mobile, and Edge written by Anirudh Koul and published by "O'Reilly Media, Inc.". This book was released on 2019-10-14 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Book Synopsis Optimization in Machine Learning and Applications by : Anand J. Kulkarni
Download or read book Optimization in Machine Learning and Applications written by Anand J. Kulkarni and published by Springer Nature. This book was released on 2019-11-29 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
