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In the dynamic world of technology, Python stands as an ever-evolving and versatile programming language that has become the go-to choice for developers, data scientists, and engineers alike. Its elegant syntax and extensive library support have propelled it to the forefront of the coding landscape, making it an indispensable tool for a wide range of applications. Python's rise to prominence is a testament to its simplicity and power, making it accessible to b
In the dynamic world of technology, Python stands as an ever-evolving and versatile programming language that has become the go-to choice for developers, data scientists, and engineers alike. Its elegant syntax and extensive library support have propelled it to the forefront of the coding landscape, making it an indispensable tool for a wide range of applications. Python's rise to prominence is a testament to its simplicity and power, making it accessible to beginners while remaining a valuable asset for seasoned professionals.
This book serves as a comprehensive guide to Python programming, tailored to accommodate both novices and experienced coders. Whether you're a curious enthusiast looking to dip your toes into the world of programming or a seasoned developer seeking to expand your skill set, this book will provide you with the essential knowledge and practical expertise to navigate the Python ecosystem with confidence. Through clear explanations, real-world examples, and hands-on exercises, you'll embark on a journey to harness the full potential of Python, enabling you to tackle diverse challenges, from web development and data analysis to machine learning and automation. Welcome to the exciting realm of Python programming; let's embark on this educational adventure together.
BOOLEAN ALGEBRA AND LOGIC GATES, COMBINATIONAL LOGIC, SYNCHRONOUS SEQUENTIAL LOGIC, ASYNCHRONOUS SEQUENTIAL LOGIC, MEMORY AND PROGRAMMABLE LOGIC.
BOOLEAN ALGEBRA AND LOGIC GATES, COMBINATIONAL LOGIC, SYNCHRONOUS SEQUENTIAL LOGIC, ASYNCHRONOUS SEQUENTIAL LOGIC, MEMORY AND PROGRAMMABLE LOGIC.
Chapter I: Principles and Foundations of IoT and AI
Chapter II: Data Access and Distributed Processing for IoT
Chapter III: Machine Learning for IoT
Chapter IV: Reinforcement Learning for IoT Introduction
Chapter V: Distributed AI for IoT
Chapter VI: Personal and Home IoT
Chapter VII: AI for the Industrial IoT
Chapter VIII: AI for Smart Cities IoT
Chapter I: Principles and Foundations of IoT and AI
Chapter II: Data Access and Distributed Processing for IoT
Chapter III: Machine Learning for IoT
Chapter IV: Reinforcement Learning for IoT Introduction
Chapter V: Distributed AI for IoT
Chapter VI: Personal and Home IoT
Chapter VII: AI for the Industrial IoT
Chapter VIII: AI for Smart Cities IoT
This book aims to cater to beginners who look to learning C and data structure under the same umbrella. While teaching C and data structure, we felt the need for a balanced book on the subject. In fact, this is the main impetus for writing such a book.
The book is designed for a one-semester course or a one-year course. It is suitable for courses based on algorithms and data structures. The prerequisite for using this text is elementary to middle level
This book aims to cater to beginners who look to learning C and data structure under the same umbrella. While teaching C and data structure, we felt the need for a balanced book on the subject. In fact, this is the main impetus for writing such a book.
The book is designed for a one-semester course or a one-year course. It is suitable for courses based on algorithms and data structures. The prerequisite for using this text is elementary to middle level knowledge of C programming.
Algorithms in the book are presented in a way that readers can easily understand the method of solving problems. Concepts are illustrated through examples. All programs in the text are tested. Each chapter ends with exercises containing questions of varied difficulty ...
Digital electronics is essential to understanding the design and working of a wide range of applications, from consumer and industrial electronics to communications; from embedded systems, and computers to security and military equipment. As the devices used in these applications decrease in size and employ more complex technology, it is essential for engineers and students to fully understand both the fundamentals and also the implementation and application p
Digital electronics is essential to understanding the design and working of a wide range of applications, from consumer and industrial electronics to communications; from embedded systems, and computers to security and military equipment. As the devices used in these applications decrease in size and employ more complex technology, it is essential for engineers and students to fully understand both the fundamentals and also the implementation and application principles of digital electronics, devices and integrated circuits, thus enabling them to use the most appropriate and effective technique to suit their technical needs.
Cyber security is an incredibly important issue that is constantly changing, with new methods, processes, and technologies coming online all the time. Books like this are invaluable to professionals working in this area, to stay abreast of all of these changes.
Cyber security is an incredibly important issue that is constantly changing, with new methods, processes, and technologies coming online all the time. Books like this are invaluable to professionals working in this area, to stay abreast of all of these changes.
Data Mining is the process of analyzing large amount of data in search of previously undiscovered business patterns. Data Warehousing is a relational/multidimensional database that is designed for Query and Analysis rather than Transaction Processing. This book provides a systematic introduction to the principles of Data Mining and Data Warehousing. It covers the entire range of data mining algorithms (prediction, classification, and association), data mining
Data Mining is the process of analyzing large amount of data in search of previously undiscovered business patterns. Data Warehousing is a relational/multidimensional database that is designed for Query and Analysis rather than Transaction Processing. This book provides a systematic introduction to the principles of Data Mining and Data Warehousing. It covers the entire range of data mining algorithms (prediction, classification, and association), data mining products and applications, stages
Data communications and networking may be the fastest growing technologies in our culture today. One of the ramifications of that growth is a dramatic increase in the number of professions where an understanding of these technologies is essential for successand a proportionate increase in the number and types of students taking courses to learn about them.
Data communications and networking may be the fastest growing technologies in our culture today. One of the ramifications of that growth is a dramatic increase in the number of professions where an understanding of these technologies is essential for successand a proportionate increase in the number and types of students taking courses to learn about them.
This book has one goal: to provide a comprehensive introduction to the theoretical and practical aspects of blockchain technology. This book contains all the material that is required to fully understand blockchain technology. After reading this book, readers will be able to develop a deep understanding of inner workings of blockchain technology and will be able to develop blockchain applications. This book covers all topics relevant to blockchain technology,
This book has one goal: to provide a comprehensive introduction to the theoretical and practical aspects of blockchain technology. This book contains all the material that is required to fully understand blockchain technology. After reading this book, readers will be able to develop a deep understanding of inner workings of blockchain technology and will be able to develop blockchain applications. This book covers all topics relevant to blockchain technology, including cryptography, cryptocurrenices, Bitcoin, Ethereum, and various other platforms and tools used for blockchain development.
It is recommended that readers have a basic understanding of computer science and basic programming experience in order to benefit fully from this book. However, if that is not the case then still this book can be read easily, as relevant background material is provided where necessary.
If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down
If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.
For many years the course Digital Signal processing was offered as a postgraduate course with students required to have a background in telecommunications (spectral analysis), circuit theory and of course Mathematics. The course provided the foundation to do more advanced research in the field. Though this was very useful it did not provide all the necessary background that many industries required; to write efficient programs and to develop applications. In m
For many years the course Digital Signal processing was offered as a postgraduate course with students required to have a background in telecommunications (spectral analysis), circuit theory and of course Mathematics. The course provided the foundation to do more advanced research in the field. Though this was very useful it did not provide all the necessary background that many industries required; to write efficient programs and to develop applications. In many institutions a simplified version of the postgraduate course has filtered into the undergraduate programme. In many cases that we have examined this course is a simplified version of the postgraduate course, it is very theoretical and does not pass the necessary tools to students that industry requires. This book is an attempt to bridge the gap. It is aimed at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware.
Recent years have seen the rise of machine learning, the study of software that learns from experience. While machine learning is a new discipline, it has found many applications. We rely on some of these applications daily; in some cases, their successes have already rendered them mundane. Many other applications have only recently been conceived, and hint at machine learning's potential. In this book, we will examine several machine learning models and
Recent years have seen the rise of machine learning, the study of software that learns from experience. While machine learning is a new discipline, it has found many applications. We rely on some of these applications daily; in some cases, their successes have already rendered them mundane. Many other applications have only recently been conceived, and hint at machine learning's potential. In this book, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly applied to, and learn to measure the performance of machine learning systems. We will work with a popular library for the Python programming language called scikit-learn, which has assembled excellent implementations of many machine learning models and algorithms under a simple yet versatile API.
Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this boo
Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this book, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. The applications of machine learning are endless and, with the amount of data available today, mostly limited by your imagination.
The great advancements in the design of microchips, digital systems, and computer hardware over the past 40 years have given birth to digital signal processing (DSP) which has grown over the years into a ubiquitous, multifaceted, and indispensable subject of study. As such DSP has been applied in most disciplines ranging from engineering to economics and from astronomy to molecular biology. Consequently, it would take a multivolume encyclopedia to cover all th
The great advancements in the design of microchips, digital systems, and computer hardware over the past 40 years have given birth to digital signal processing (DSP) which has grown over the years into a ubiquitous, multifaceted, and indispensable subject of study. As such DSP has been applied in most disciplines ranging from engineering to economics and from astronomy to molecular biology. Consequently, it would take a multivolume encyclopedia to cover all the facets, aspects, and ramifications of DSP, and such a treatise would require many authors. This textbook focuses instead on the fundamentals of DSP, namely, on the representation of signals by mathematical models and on the processing of signals by discrete-time systems. Various types of processing are possible for signals but the processing of interest in this volume is almost always linear and it typically involves reshaping, transforming, or manipulating the frequency spectrum of the signal of interest. The prerequisite knowledge for the book is a typical undergraduate mathematics background of calculus, complex analysis, and simple differential equations. At certain universities, complex analysis may not be included in the curriculum. To overcome this difficulty, the basics of complex analysis are summarized in Appendix A which can also serve as a quick reference or refresher.
This is a book for people who want to learn about the Julia language with a view to using it for data science. Some effort has gone into making this book suitable for someone who has familiarity with the R software and wants to learn about Julia. However, prior knowledge of R is not a requirement. While this book is not intended as a textbook for a course, some may find it a useful book to follow for a course that introduces statistics or data science students
This is a book for people who want to learn about the Julia language with a view to using it for data science. Some effort has gone into making this book suitable for someone who has familiarity with the R software and wants to learn about Julia. However, prior knowledge of R is not a requirement. While this book is not intended as a textbook for a course, some may find it a useful book to follow for a course that introduces statistics or data science students to Julia. It is our sincere hope that students, researchers and data scientists in general, who wish to learn Julia, will find this book beneficial.
This book has been written in a more comprehensive and concise manner suitable for undergraduate students. Moreover, recent and advanced topics have also been included for which students have to refer foreign books quite often. Inclusion of some recent topics particularly self-compacting concrete, high performance concrete, technology of concrete, some sophisticated and special techniques in concrete technology, use of waste materials in concrete are definitel
This book has been written in a more comprehensive and concise manner suitable for undergraduate students. Moreover, recent and advanced topics have also been included for which students have to refer foreign books quite often. Inclusion of some recent topics particularly self-compacting concrete, high performance concrete, technology of concrete, some sophisticated and special techniques in concrete technology, use of waste materials in concrete are definitely some of the attractions of the book. This comprehensive book will serve as a text and guide book to undergraduate and even postgraduate students, concrete technologists, material scientists, practicing engineers and all users of concrete.
The goal of this book is to teach you the Python programming language, learning to program the same way we did: by creating simple games. There’s something more exciting about learning to program by writing software that’s fun. And even though the examples are entertaining, you’ll still see some serious programming. we cover all of the fundamental topics you’d expect from an introductory text and then some. In addition, we point out con
The goal of this book is to teach you the Python programming language, learning to program the same way we did: by creating simple games. There’s something more exciting about learning to program by writing software that’s fun. And even though the examples are entertaining, you’ll still see some serious programming. we cover all of the fundamental topics you’d expect from an introductory text and then some. In addition, we point out concepts and techniques that you can apply to more mainstream projects. If you’re new to programming, you’ve made the right choice. Python is the perfect beginners’ language. It has a clear and simple syntax that will get you writing useful programs in short order. Python even has an interactive mode, which offers immediate feedback, allowing you to test out new ideas almost instantly. If you’ve done some programming
before, you’ve still made the right choice.
Welcome to this text book to advanced machine learning using Python. It's possible that you've picked this up with some initial interest, but aren't quite sure what to expect. In a nutshell, there has never been a more exciting time to learn and use machine learning techniques, and working in the field is only getting more rewarding. If you want to get up-to-speed with some of the more advanced data modeling techniques and gain expe
Welcome to this text book to advanced machine learning using Python. It's possible that you've picked this up with some initial interest, but aren't quite sure what to expect. In a nutshell, there has never been a more exciting time to learn and use machine learning techniques, and working in the field is only getting more rewarding. If you want to get up-to-speed with some of the more advanced data modeling techniques and gain experience using them to solve challenging problems, this is a good book for you.
Ongoing advances in computational power (per Moore's Law) have begun to make machine learning, once mostly a research discipline, more viable in commercial contexts. This has caused an explosion of new applications and new or rediscovered techniques, catapulting the obscure concepts of data science, AI, and machine learning into the public consciousness and strategic planning of companies internationally.
Data communications and networking presents a unique challenge for instructors and students. As both the number and types of students are increasing, it is essential to have a textbook that provides coverage of the latest advances, while presenting the material in a way that is accessible to students with little. Using a bottom-up approach, Data Communications and Networking presents this highly technical subject matter without relying on complex formulas by u
Data communications and networking presents a unique challenge for instructors and students. As both the number and types of students are increasing, it is essential to have a textbook that provides coverage of the latest advances, while presenting the material in a way that is accessible to students with little. Using a bottom-up approach, Data Communications and Networking presents this highly technical subject matter without relying on complex formulas by using a strong pedagogical approach supported by more than 100 figures.
Now in its Second Edition, this textbook brings the beginning student right to the forefront of the latest advances in the field, while presenting the fundamentals in a clear, straightforward manner. Students will find better coverage, improved figures and better explanations on cutting-edge material. The "bottom-up" approach allows instructors to cover the material in one course, rather than having separate courses on data communications and networking.
Artificial Intelligence starts by helping you understand AI, especially what AI needs to work and why it has failed in the past. You also discover the basis for some of the issues with AI today and how those issues might prove to be nearly impossible to solve in some cases. Of course, along with the issues, you also dis-cover the fixes for some problems and consider where scientists are taking AI in search of answers. For a technology to survive, it must
Artificial Intelligence starts by helping you understand AI, especially what AI needs to work and why it has failed in the past. You also discover the basis for some of the issues with AI today and how those issues might prove to be nearly impossible to solve in some cases. Of course, along with the issues, you also dis-cover the fixes for some problems and consider where scientists are taking AI in search of answers. For a technology to survive, it must have a group of solid applications that actually work. It also must provide a payback to investors with the foresight to invest in the technology. In the past, AI failed to achieve critical success because it lacked some of these features. AI also suffered from being ahead of its time: True AI needed to wait for the current hardware to actually succeed. Today, you can find AI used in various computer applications and to automate processes. It’s also relied on heavily in the medical field and to help improve human interaction. AI is also related to data analysis, machine learning, and deep learning. AI has a truly bright future today because it has become an essential technology. This book also shows you the paths that AI is likely to follow in the future. The various trends discussed in this book are based on what people are actually trying to do now. The new technology hasn’t succeeded yet, but because people are working on it, it does have a good chance of success at some point.
Computer vision as a field is an intellectual frontier. Like any frontier, it is exciting and disorganized, and there is often no reliable authority to appeal to. Many useful ideas have no theoretical grounding, and some theories are useless in practice; developed areas are widely scattered, and often one looks completely inaccessible from the other. Nevertheless, we have attempted in this book to present a fairly orderly picture of the field. We see comp
Computer vision as a field is an intellectual frontier. Like any frontier, it is exciting and disorganized, and there is often no reliable authority to appeal to. Many useful ideas have no theoretical grounding, and some theories are useless in practice; developed areas are widely scattered, and often one looks completely inaccessible from the other. Nevertheless, we have attempted in this book to present a fairly orderly picture of the field. We see computer vision—or just “vision”; apologies to those who study human or animal vision—as an enterprise that uses statistical methods to disentangle data using models constructed with the aid of geometry, physics, and learning theory.
Deep learning has taken the world by surprise, driving rapid progress in such diverse fields as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and biomedical informatics. Moreover, the success of deep learning on so many tasks of practical interest has even catalyzed developments in theoretical machine learning and statistics. With these advances in hand, we can now build cars that drive themselves with more
Deep learning has taken the world by surprise, driving rapid progress in such diverse fields as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and biomedical informatics. Moreover, the success of deep learning on so many tasks of practical interest has even catalyzed developments in theoretical machine learning and statistics. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences—from astrophysics to biology.
The topic of this book is Straight Talk to data science, which is a field of study and application that has been growing rapidly for the past several decades. As a growing field, it is gaining a lot of attention in both the media as well as in the job market. This move was modeled after tech companies who, honestly, only recently started hiring massive data teams. These skills are in high demand and their applications extend much further than today's job marke
The topic of this book is Straight Talk to data science, which is a field of study and application that has been growing rapidly for the past several decades. As a growing field, it is gaining a lot of attention in both the media as well as in the job market. This move was modeled after tech companies who, honestly, only recently started hiring massive data teams. These skills are in high demand and their applications extend much further than today's job market. This book will attempt to bridge the gap between math/programming/domain expertises. Most people today have expertise in at least one of these (maybe two), but proper data science requires a little bit of all three. We will dive into topics from all three areas and solve complex problems. We will clean, explore, and analyze data in order to derive scientific and accurate conclusions. Machine learning and deep learning techniques will be applied to solve complex data tasks.
Welcome to one of the great adventures of the twenty-first century. Programming requires little in the way of specialized equipment; the software tools can all be downloaded for free off the Internet, and it can be practiced in the safety and comfort of your own home, without having to ask anyone’s permission. This chapter will ease you in gently by introducing you to the software you will need to create your programs: a command-line interface, which all
Welcome to one of the great adventures of the twenty-first century. Programming requires little in the way of specialized equipment; the software tools can all be downloaded for free off the Internet, and it can be practiced in the safety and comfort of your own home, without having to ask anyone’s permission. This chapter will ease you in gently by introducing you to the software you will need to create your programs: a command-line interface, which allows you to use Python in interactive mode, and a text editor for writing scripts—nothing more complicated than that. I will also show you where to go to find help and documentation, so you can decode the sometimes impenetrable jargon that seems to surround this, the geekiest of all technical disciplines. To begin with, you will need to make sure that you have a decently recent version of Python installed on your machine or follow the steps later in this chapter to install it (see “Choosing the Right Python Version” for a definition of decently recent). This chapter explains how to make sure that you have everything set up correctly and that you have suitable references at hand before you start your journey.
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