Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable programming language such as JavaScript to work with more approachable, fundamental coding ideas. Using JavaScript programming features along with standard libraries, you'll first learn to design and develop interactive graphics applications. Then move further into neural systems and human pose estimation strategies. For training and deploying your ML models in the browser, TensorFlow.js libraries will be emphasized. After conquering the fundamentals, you'll dig into the wilderness of ML. Employ the ML and Processing (P5) libraries for Human Gait analysis. Building up Gait recognition with themes, you'll come to understand a variety of ML implementation issues. For example, you'll learn about the classification of normal and abnormal Gait patterns. With Beginning Machine Learning in the Browser, you'll be on your way to becoming an experienced Machine Learning developer. What You'll Learn Work with ML models, calculations, and information gathering Implement TensorFlow.js libraries for ML models Perform Human Gait Analysis using ML techniques in the browser Who This Book Is For Computer science students and research scholars, and novice programmers/web developers in the domain of Internet Technologies

This book includes selected papers from the 2nd International Conference on Machine Learning and Information Processing (ICMLIP 2020), held at Vardhaman College of Engineering, Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, from November 28 to 29, 2020. It presents the latest developments and technical solutions in the areas of advanced computing and data sciences, covering machine learning, artificial intelligence, human-computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.

This two-volume set constitutes the refereed proceedings of the 16th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020, held in Shanghai, China, in October 2020.The 61 full papers and 16 short papers presented were carefully reviewed and selected from 211 submissions. The papers reflect the conference sessions as follows: Collaborative Applications for Network and E-Commerce; Optimization for Collaborate System; Cloud and Edge Computing; Artificial Intelligence; AI Application and Optimization; Classification and Recommendation; Internet of Things; Collaborative Robotics and Autonomous Systems; Smart Transportation.

This book includes high impact papers presented at the International Conference on Communication, Computing and Electronics Systems 2019, held at the PPG Institute of Technology, Coimbatore, India, on 15-16 November, 2019. Discussing recent trends in cloud computing, mobile computing, and advancements of electronics systems, the book covers topics such as automation, VLSI, embedded systems, integrated device technology, satellite communication, optical communication, RF communication, microwave engineering, artificial intelligence, deep learning, pattern recognition, Internet of Things, precision models, bioinformatics, and healthcare informatics.

This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019). It focuses on both theory and applications in the broad areas of big data and machine learning. It brings together the academia, researchers, developers and practitioners from scientific organizations and industry to share and disseminate recent research findings.

Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning. Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office. What You Will Learn Take advantage of the built-in support of the Google Colab ecosystem Work with TensorFlow data sets Create input pipelines to feed state-of-the-art deep learning models Create pipelined state-of-the-art deep learning models with clean and reliable Python code Leverage pre-trained deep learning models to solve complex machine learning tasks Create a simple environment to teach an intelligent agent to make automated decisions Who This Book Is For Readers who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab

The book discusses major technical advances and research findings in the field of machine intelligence in medical image analysis. It examines the latest technologies and that have been implemented in clinical practice, such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy. This book provides insights into the basic science involved in processing, analysing, and utilising all aspects of advanced computational intelligence in medical decision-making based on medical imaging.

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.

This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains, machine learning, intelligent information retrieval and business intelligence, intelligent information system development using design science principles, intelligent web mining and knowledge discovery systems.

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health, transportation and Internet applications

This book contains the topics of artificial intelligence and deep learning that do have much application in real-life problems. The concept of uncertainty has long been used in applied science, especially decision making and a logical decision must be made in the field of uncertainty or in the real-life environment that is formed and combined with vague concepts and data. The chapters of this book are connected to the new concepts and aspects of decision making with uncertainty. Besides, other chapters are involved with the concept of data mining and decision making under uncertain computations.

This two-volume book constitutes the post-conference proceedings of the 5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021, held in Nashik, India, in April 2021.* The 103 full papers were carefully reviewed and selected from 781 submissions. The papers in Part I and II are centered around topics like distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations database management system engines, data mining, information retrieval query processing, database and storage security, ubiquitous and mobile computing, parallel computing methodologies, and others. *The conference was held virtually due to the COVID-19 pandemic.

Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. What You Will Learn Perform basic data analysis and construct models in scikit-learn and PySpark Train, test, and validate your models (hyperparameter tuning) Know what MLOps is and what an ideal MLOps setup looks like Easily integrate MLFlow into your existing or future projects Deploy your models and perform predictions with them on the cloud Who This Book Is For Data scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models

This book features selected papers presented at Second International Conference on International Conference on Information Management & Machine Intelligence (ICIMMI 2020) held at Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India during 24 - 25 July 2020. It covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation; applications of artificial intelligence in soft computing and pattern recognition; cloud-based applications for machine learning; application of IoT in power distribution systems; as well as wireless sensor networks and adaptive wireless communication.

This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this exciting field, including the pioneering work of Rudolf Carnap, Warren McCulloch, Walter Pitts, Bulcsu Laszlo, and Geoffrey Hinton. Topics and features: Provides a brief history of mathematical logic, and discusses the critical role of philosophy, psychology, and neuroscience in the history of AI Presents a philosophical case for the use of fuzzy logic approaches in AI Investigates the similarities and differences between the Word2vec word embedding algorithm, and the ideas of Wittgenstein and Firth on linguistics Examines how developments in machine learning provide insights into the philosophical challenge of justifying inductive inferences Debates, with reference to philosophical anthropology, whether an advanced general artificial intelligence might be considered as a living being Investigates the issue of computational complexity through deep-learning strategies for understanding AI-complete problems and developing strong AI Explores philosophical questions at the intersection of AI and transhumanism This inspirational volume will rekindle a passion for deep learning in those already experienced in coding and studying this discipline, and provide a philosophical big-picture perspective for those new to the field.

This book constitutes the thoroughly refereed papers of the 4th International Conference on Applied Informatics, ICAI 2021, held in Buenos Aires, Argentina, in October, 2021.The 35 full papers were carefully reviewed and selected from 89 submissions. The papers are organized in topical sections on artificial intelligence; data analysis; decision systems; health care information systems; image processing; security services; simulation and emulation; smart cities; software and systems modeling; software design engineering.

This edited book provides an optimal portrayal of the principles and applications related to network security. The book is thematically divided into five segments: Part A describes the introductory issues related to network security with some concepts of cutting-edge technologies; Part B builds from there and exposes the readers to the digital, cloud and IoT forensics; Part C presents readers with blockchain and cryptography techniques; Part D deals with the role of AI and machine learning in the context of network security. And lastly, Part E is written on different security networking methodologies. This is a great book on network security, which has lucid and well-planned chapters. All the latest security technologies are thoroughly explained with upcoming research issues. Details on Internet architecture, security needs, encryption, cryptography along with the usages of machine learning and artificial intelligence for network security are presented in a single cover. The broad-ranging text/reference comprehensively surveys network security concepts, methods, and practices and covers network security policies and goals in an integrated manner. It is an essential security resource for practitioners in networks and professionals who develop and maintain secure computer networks.

Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract. By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning-Specialty certification exam. What You Will Learn Be familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud Formation Understand SageMaker, Amazon Comprehend, and Amazon Forecast Execute live projects: from the pre-processing phase to deployment on AWS Who This Book Is For Machine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification

Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You'll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. What You'll Learn Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices Who This Book Is For Developers with Swift programming experience who would like to learn convolutional neural networks by example using Swift for Tensorflow as a starting point.

This book presents the latest advances in photometric 3D reconstruction. It provides the reader with an overview of the state of the art in the field, and of the latest research into both the theoretical foundations of photometric 3D reconstruction and its practical application in several fields (including security, medicine, cultural heritage and archiving, and engineering). These techniques play a crucial role within such emerging technologies as 3D printing, since they permit the direct conversion of an image into a solid object. The book covers both theoretical analysis and real-world applications, highlighting the importance of deepening interdisciplinary skills, and as such will be of interest to both academic researchers and practitioners from the computer vision and mathematical 3D modeling communities, as well as engineers involved in 3D printing. No prior background is required beyond a general knowledge of classical computer vision models, numerical methods for optimization, and partial differential equations.

This book covers different aspects of real-world applications of optimization algorithms. It provides insights from the Sixth International Conference on Harmony Search, Soft Computing and Applications held at Istanbul University, Turkey, in July 2020. Harmony Search (HS) is one of the most popular metaheuristic algorithms, developed in 2001 by Prof. Joong Hoon Kim and Prof. Zong Woo Geem, that mimics the improvisation process of jazz musicians to seek the best harmony. The book consists of research articles on novel and newly proposed optimization algorithms; the theoretical study of nature-inspired optimization algorithms; numerically established results of nature-inspired optimization algorithms; and real-world applications of optimization algorithms and synthetic benchmarking of optimization algorithms.

This book reports on new theories and applications in the field of intelligent systems and computing. It covers cutting-edge computational and artificial intelligence methods, advances in computer vision, big data, cloud computing, and computation linguistics, as well as cyber-physical and intelligent information management systems. The respective chapters are based on selected papers presented at the workshop on intelligent systems and computing, held during the International Conference on Computer Science and Information Technologies, CSIT 2020, which was jointly organized on September 23-26, 2020, by the Lviv Polytechnic National University, Ukraine, the Kharkiv National University of Radio Electronics, Ukraine, and the Technical University of Lodz, Poland, under patronage of Ministry of Education and Science of Ukraine. Given its breadth of coverage, the book provides academics and professionals with extensive information and a timely snapshot of the field of intelligent systems, and is sure to foster new discussions and collaborations among different groups.

This book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 15-16, 2021. Extreme learning machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles' filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that "random hidden neurons" capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. This conference provides a forum for academics, researchers, and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms, and applications of ELM. It gives readers a glance of the most recent advances of ELM.

With the intriguing development of technologies in several industries, along with the advent of ubiquitous computational resources, there are now ample opportunities to develop innovative computational technologies in order to solve a wide range of issues concerning uncertainty, imprecision, and vagueness in various real-life problems. The challenge of blending modern computational techniques with traditional computing methods has inspired researchers and academics alike to focus on developing innovative computational techniques. In the near future, computational techniques may provide vital solutions by effectively using evolving technologies such as computer vision, natural language processing, deep learning, machine learning, scientific computing, and computational vision. A vast number of intelligent computational algorithms are emerging, along with increasing computational power, which has significantly expanded the potential for developing intelligent applications. These proceedings of the International Conference on Inventive Computation Technologies [ICICT 2019] cover innovative computing applications in the areas of data mining, big data processing, information management, and security.

This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.