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!
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 guidance AI 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.
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 examples Concepts of image formation OpenCV installation with step-by-step screen shots Concepts behind intensity, brightness and contrast, color models Ways by which noises are created in an image and the possible remedial measures Edge detection, image segmentation, classification, regression, classification algorithms Importance of frequency domain in image processing field Relevant code snippets and the MATLAB® codes, and several interesting sets of simple programs in OpenCV and Python to aid learning and for complete understanding The 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.
Introduction To C Programming 2. Conditional Constructs And Looping 3. Arrays 4. Operators 5.Functions 6. Storage Qualifiers 7. Pointers 8. Structures And Unions 9. Strings 10. File Handling In C 11. Data Structures Using C 12. Command Line Arguments 13. C Programming In Unix/Linux 14. Pre-Processor Directives 15. Coding Guideline
Unique selling point: • This book teaches readers how to build IoT products through practical projects, ranging from beginners to advanced level, so that they can build knowledge through hands-on tasks rather than theoretical knowledge. Core audience: • IoT enthusiasts, students and tutors Place in the market: • This is a new approach and distinctly different from all of the books which focus only on theory.
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.
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.
This book has been specifically designed to orient students for the technical interview in Operating System concepts. This book has been written to help students undertake a thorough and objective preparation. The contents have been carefully handpicked from authors' experience and expertise developed by being either direct witness to the recruitment process, mentoring students in their colleges, myriad discussion with peers who are placement organizers in colleges, hours of studying discussions on the topics in social media, blogs and dedicated web sites, and so it is no wonder that the book is highly focused for interview preparation in Operating System. This book includes the latest topics and questions discussed in top companies today while continuing to maintain the emphasis on basic concepts along with problem solving skills for both students preparing for exams and for interviewees.
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