Shriram K Vasudevan

The book provides a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives are to offer an overview to the elementary concepts and practices appropriate to digital image processing as well as to provide theoretical exposition. It starts with an expanded coverage of the fundamentals to provide a more comprehensive and cohesive coverage of the topics including but not limited to: Applications and tools for image processing, and fundamentals with several implementation examplesConcepts of image formationOpenCV installation with step-by-step screen shotsConcepts behind intensity, brightness and contrast, color modelsWays by which noises are created in an image and the possible remedial measuresEdge detection, image segmentation, classification, regression, classification algorithmsImportance of frequency domain in image processing fieldRelevant code snippets and the MATLAB® codes, and several interesting sets of simple programs in OpenCV and Python to aid learning and for complete understandingThe video lectures for specific topics through YouTube enable easy inference for the readers to apply the learnt theory into practice. The addition of contents at the end of each chapter such as quizzes and review questions fully prepare the readers for further study.Graduate students, post graduate students, researchers, and anyone in general interested in image processing, computer vision, machine learning domains etc. can find this book an excellent starting point for information and an able ally.

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively.Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidanceAI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

Python’s simplicity and versatility make it an ideal language for both beginners and experienced programmers. Its syntax facilitates a smooth learning curve, enabling individuals to concentrate on grasping programming concepts instead of wrestling with intricate syntax rules. The extensive standard library reinforces its practicality, offering pre-built modules and functions that reduce manual coding efforts. Python’s versatility is evident in its applications, spanning web development, data analysis, Machine Learning and automation.The language’s interactive nature supports real-time code experimentation, stepping up the learning process and enhancing understanding. Python’s wealth of online resources further enriches the learning experience, fostering a community where individuals can develop their programming skills. Python: A Practical Learning Approach exemplifies Python’s simplicity and versatility with numerous examples, ensuring a seamless learning journey. Beyond theory, the language’s practicality allows learners to actively apply their knowledge in real-world scenarios, establishing Python as an asset in education.

Python’s simplicity and versatility make it an ideal language for both beginners and experienced programmers. Its syntax facilitates a smooth learning curve, enabling individuals to concentrate on grasping programming concepts instead of wrestling with intricate syntax rules. The extensive standard library reinforces its practicality, offering pre-built modules and functions that reduce manual coding efforts. Python’s versatility is evident in its applications, spanning web development, data analysis, Machine Learning and automation.The language’s interactive nature supports real-time code experimentation, stepping up the learning process and enhancing understanding. Python’s wealth of online resources further enriches the learning experience, fostering a community where individuals can develop their programming skills. Python: A Practical Learning Approach exemplifies Python’s simplicity and versatility with numerous examples, ensuring a seamless learning journey. Beyond theory, the language’s practicality allows learners to actively apply their knowledge in real-world scenarios, establishing Python as an asset in education.

Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.

Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.

oneAPI is a unified programming model and software development kit (SDK) from Intel that empowers software developers to generate high-performance applications that can run on different devices, comprising CPUs, GPUs, FPGAs, and other accelerators. It lets developers write code once and deploy it on multiple architectures, decreasing the complexity as well as the cost and time of software development. One of the significant strengths of oneAPI is in its capability to support an eclectic range of devices and architectures, including artificial intelligence, high-performance computing, and data analytics. Along with libraries, tools, and compilers, oneAPI makes it cool for developers to create optimized code for an extensive variety of applications, making it an indispensable tool for any developer who wants to create high-performance software and reap the benefit of the latest hardware technologies. The versatility of oneAPI, by means of appropriate theory and practical implementation with the latest tools in machine learning, has been presented in a simple yet effective way in this book that caters to everyone’s needs. Come on, let’s unleash the true power of our code across varied architectures!

oneAPI is a unified programming model and software development kit (SDK) from Intel that empowers software developers to generate high-performance applications that can run on different devices, comprising CPUs, GPUs, FPGAs, and other accelerators. It lets developers write code once and deploy it on multiple architectures, decreasing the complexity as well as the cost and time of software development. One of the significant strengths of oneAPI is in its capability to support an eclectic range of devices and architectures, including artificial intelligence, high-performance computing, and data analytics. Along with libraries, tools, and compilers, oneAPI makes it cool for developers to create optimized code for an extensive variety of applications, making it an indispensable tool for any developer who wants to create high-performance software and reap the benefit of the latest hardware technologies. The versatility of oneAPI, by means of appropriate theory and practical implementation with the latest tools in machine learning, has been presented in a simple yet effective way in this book that caters to everyone’s needs. Come on, let’s unleash the true power of our code across varied architectures!

The book provides a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives are to offer an overview to the elementary concepts and practices appropriate to digital image processing as well as to provide theoretical exposition. It starts with an expanded coverage of the fundamentals to provide a more comprehensive and cohesive coverage of the topics including but not limited to: Applications and tools for image processing, and fundamentals with several implementation examplesConcepts of image formationOpenCV installation with step-by-step screen shotsConcepts behind intensity, brightness and contrast, color modelsWays by which noises are created in an image and the possible remedial measuresEdge detection, image segmentation, classification, regression, classification algorithmsImportance of frequency domain in image processing fieldRelevant code snippets and the MATLAB® codes, and several interesting sets of simple programs in OpenCV and Python to aid learning and for complete understandingThe video lectures for specific topics through YouTube enable easy inference for the readers to apply the learnt theory into practice. The addition of contents at the end of each chapter such as quizzes and review questions fully prepare the readers for further study.Graduate students, post graduate students, researchers, and anyone in general interested in image processing, computer vision, machine learning domains etc. can find this book an excellent starting point for information and an able ally.

Internet of Things (IoT) stands acclaimed as a widespread area of research and has definitely enticed the interests of almost the entire globe. IoT appears to be the present as well as the future technology. This book attempts to inspire readers to explore and become accustomed to IoT. Presented in a lucid and eloquent way, this book adopts a clear and crisp approach to impart the basics as expeditiously as possible. It kicks off with the very fundamentals and then seamlessly advances in such a way that the step-by-step unique approach, connection layout, and the verified codes provided for every project can enhance the intuitive learning process and will get you onboard to the world of product building. We can assure that you will be definitely raring to start developing your own IoT solutions and to get yourself completely lost in the charm of IoT.Let’s start connecting the unconnected! It’s time to get IoT-fied.

Internet of Things (IoT) stands acclaimed as a widespread area of research and has definitely enticed the interests of almost the entire globe. IoT appears to be the present as well as the future technology. This book attempts to inspire readers to explore and become accustomed to IoT. Presented in a lucid and eloquent way, this book adopts a clear and crisp approach to impart the basics as expeditiously as possible. It kicks off with the very fundamentals and then seamlessly advances in such a way that the step-by-step unique approach, connection layout, and the verified codes provided for every project can enhance the intuitive learning process and will get you onboard to the world of product building. We can assure that you will be definitely raring to start developing your own IoT solutions and to get yourself completely lost in the charm of IoT.Let’s start connecting the unconnected! It’s time to get IoT-fied.

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively.Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidanceAI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

This book intends to develop cyber awareness and technical knowledge in anyone who is interested in technology by looking at subjects and experiences the average person will have come into contact with in their life. This book aims to provide a complete and comprehensive analysis, technological inputs and case studies for the readers to build their awareness and knowledge, but in a meaningful way which will stay relevant. There are books available on the market, but they primarily discuss theory, and no industry connection or current state-of-the-art technology is presented. By discussing subjects and experiences that all readers will be familiar with, this book will aid understanding and comprehension of how cyber threats can be noticed, avoided and understood in everyday life. As well as case studies, this book also contains plentiful illustrations and supplementary videos, which will be available via YouTube to complement the information.Giri Govindarajulu is a Chief Information Security officer for Cisco Asiapac and is a 20-year Cisco veteran.Shyam Sundar Ramaswami is the Lead Threat Researcher with the Cisco Talos Threat Intelligence group. Shyam is a two-time TEDx speaker and a teacher of cybersecurity.Dr. Shriram K. Vasudevan is currently working as Dean of K. Ramakrishnan College of Technology. He has authored/co-authored 42 books for reputed publishers across the globe and 122 research papers in revered international journals, plus 30 papers for international/national conferences.

This book intends to develop cyber awareness and technical knowledge in anyone who is interested in technology by looking at subjects and experiences the average person will have come into contact with in their life. This book aims to provide a complete and comprehensive analysis, technological inputs and case studies for the readers to build their awareness and knowledge, but in a meaningful way which will stay relevant. There are books available on the market, but they primarily discuss theory, and no industry connection or current state-of-the-art technology is presented. By discussing subjects and experiences that all readers will be familiar with, this book will aid understanding and comprehension of how cyber threats can be noticed, avoided and understood in everyday life. As well as case studies, this book also contains plentiful illustrations and supplementary videos, which will be available via YouTube to complement the information.Giri Govindarajulu is a Chief Information Security officer for Cisco Asiapac and is a 20-year Cisco veteran.Shyam Sundar Ramaswami is the Lead Threat Researcher with the Cisco Talos Threat Intelligence group. Shyam is a two-time TEDx speaker and a teacher of cybersecurity.Dr. Shriram K. Vasudevan is currently working as Dean of K. Ramakrishnan College of Technology. He has authored/co-authored 42 books for reputed publishers across the globe and 122 research papers in revered international journals, plus 30 papers for international/national conferences.

Data Structures using Python provides an introduction to design, analysis, and implementation of data structures using the powerful language Python. This book is designed for a first course on the subject. It is written for the undergraduate engineering students of Computer Science, Information Technology and allied disciplines. The book begins with an overview of the concept, need, nomenclature followed by discussion of Arrays, Stacks, Queues, Double Ended Queues, Linked List - all under a chapter on Linear Data Structures. This is followed by a chapter on Non-linear Data Structures where Heap, Hash Table, Trie, and Disjoint Sets are discussed. Trees, though a non-linear data structure, is discussed as a separate chapter to emphasize upon its importance. The last few chapters of the book discusses Graphs, Searching, and Sorting finally ending with an appendix on Python built-in class functions.