A new addition to the popular Bite-sized Chunks series, this expert-led book will explore the science and technology behind AI.

A fascinating, accessible and expertly written introduction to the most important invention in human history: the internet.

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key FeaturesGet well-versed with the ML.NET framework and its components and APIs using practical examplesLearn how to build, train, and evaluate popular machine learning algorithms with ML.NET offeringsExtend your existing machine learning models by integrating with TensorFlow and other librariesBook Description Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. What you will learnUnderstand the framework, components, and APIs of ML.NET using C#Develop regression models using ML.NET for employee attrition and file classificationEvaluate classification models for sentiment prediction of restaurant reviewsWork with clustering models for file type classificationsUse anomaly detection to find anomalies in both network traffic and login historyWork with ASP.NET Core Blazor to create an ML.NET enabled web applicationIntegrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detectionWho this book is for If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.

'An excellent starter for those who want to gain an insight into how AI works and why it's likely to shape our lives.' – The Daily Telegraph Artificial intelligence will shake up our lives as thoroughly as the arrival of the internet. This popular, up-to-date book charts AI’s rise from its Cold War origins to its explosive growth in the 2020s. Tech journalist Chris Stokel-Walker (TikTok Boom and YouTubers) goes into the laboratories of the Silicon Valley innovators making rapid advances in ‘large language models’ of machine learning. He meets the insiders at Google and OpenAI who built Gemini and ChatGPT and reveals the extraordinary plans they have for them. Along the way, he explores AI’s dark side by talking to workers who have lost their jobs to bots and engages with futurologists worried that a man-made super-intelligence could threaten humankind. He answers critical questions about the AI revolution, such as what humanity might be jeopardising and the professions that will win and lose – and whether the existential threat technologists Elon Musk and Sam Altman are warning about is realistic – or a smokescreen to divert attention away from their growing power. How AI Ate the World is a ‘start here’ guide for anyone who wants to know more about the world we have just entered. Reviews 'An excellent starter for those who want to gain an insight into how AI works and why it's likely to shape our lives.' The Daily Telegraph 'How AI Ate the World prodigiously captures the key issues and concerns around artificial intelligence.' Azeem Azhar, Exponential View 'From ancient China to Victorian England, How AI Ate The World is the story of the characters, moments, technologies, and relationships that populate the rich history of artificial intelligence... How AI Ate The World grapples with what the age of automation means for the people living through it.' Harry Law, University of Cambridge 'A witty, engaging book that takes us through AI's bumpy past to help us understand its present, and future, impacts. I highly recommend it to anyone who is impacted by AI tech – which is to say, everyone on the planet.' Sasha Luccioni, Hugging Face 'Easily the most comprehensive book on AI I have read so far, covering all the key issues' Peter Hunt, Business & Tech Correspondent, Evening Standard 'A comprehensive and compelling look at the technology that's transforming our world. It's an essential guide, full of surprises, to the technology you need to know.' Matt Navarra, social media expert 'Whether you are new to AI or have been following the AI hype for years, Chris Stokel-Walker offers an entertaining balance of history, context and insight that has something for everyone. The story of AI’s evolution is a complex one, but Stokel-Walker tackles it in a clear, direct way that will bring you up to speed while helping you grapple with what it all means — for individuals, the workplace, society and the planet.' Sharon Goldman, VentureBeat 'This book is a wild, brilliant ride through centuries of thinking about and decades of developing machines that can learn. As a crash course in how we got to this current point of thrilling chaos, it will take some beating. Whether or not you agree with Stokel-Walker’s solutions or not, How AI Ate The World is essential reading to understand where we are and how we got here' Ciaran Martin, former CEO, UK National Cyber Security Centre Buy the book to discover your future

The main objective of ICCSAI2013 is to provide a platform for the presentation of top and latest research results in global scientific areas. The conference aims to provide a high level international forum for researcher, engineers and practitioners to present and discuss recent advances and new techniques in computer science and artificial intelligence. It also serves to foster communications among researcher, engineers and practitioners working in a common interest in improving computer science, artificial intelligence and the related fields. We have received 325 numbers of papers through "Call for Paper", out of which 94 numbers of papers were accepted for publication in the conference proceedings through double blind review process. The conference is designed to stimulate the young minds including Research Scholars, Academicians, and Practitioners to contribute their ideas, thoughts and nobility in these two disciplines.

Despite the promise of competency-based education (CBE), learner-centered issues related to support, retention, and program completion rates remain problematic. In addition, the infrastructure for higher education, including issues related to faculty (intellectual property, workload, and curriculum), pose barriers and challenges in the design, development, implementation, and delivery of CBE. In response, administrators, faculty, designers, and developers of competency-based experiences must incorporate innovative strategies that are foreign to the traditional institution. A strong emphasis on retention and graduation rates must surround the student with support, starting with the design and development of the CBE system. There are few resources that can help prepare instructional designers, advisors, academic administrators, and faculty to meet the many challenges of designing, developing, implementing, and managing CBE. Career Ready Education Through Experiential Learning is an essential reference book that includes strategies for design and development of competency-based education (CBE) programs, as well as administrative and delivery strategies as examples of how CBE can be implemented. Through a strong theoretical framework, chapters present the best practices, strategies, and practical tips as examples and scenarios that can be used in higher education settings. While highlighting education courses, programs, and lessons across various institutions and educational domains, this book is ideal for higher education administrators and policy designers/implementors, instructional designers, curriculum developers, faculty, public policy leaders, students in curriculum and instruction and instructional technology programs, along with researchers and practitioners interested in CBE and experiential learning in higher education.

This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

This book provides theoretical background and state-of-the-art findings in artificial intelligence and cybersecurity for industry 4.0 and helps in implementing AI-based cybersecurity applications. Machine learning-based security approaches are vulnerable to poison datasets which can be caused by a legitimate defender's misclassification or attackers aiming to evade detection by contaminating the training data set. There also exist gaps between the test environment and the real world. Therefore, it is critical to check the potentials and limitations of AI-based security technologies in terms of metrics such as security, performance, cost, time, and consider how to incorporate them into the real world by addressing the gaps appropriately. This book focuses on state-of-the-art findings from both academia and industry in big data security relevant sciences, technologies, and applications. ​