Products related to Coding:
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Information Theory : From Coding to Learning
This enthusiastic introduction to the fundamentals of information theory builds from classical Shannon theory through to modern applications in statistical learning, equipping students with a uniquely well-rounded and rigorous foundation for further study.Introduces core topics such as data compression, channel coding, and rate-distortion theory using a unique finite block-length approach.With over 210 end-of-part exercises and numerous examples, students are introduced to contemporary applications in statistics, machine learning and modern communication theory.This textbook presents information-theoretic methods with applications in statistical learning and computer science, such as f-divergences, PAC Bayes and variational principle, Kolmogorov's metric entropy, strong data processing inequalities, and entropic upper bounds for statistical estimation.Accompanied by a solutions manual for instructors, and additional standalone chapters on more specialized topics in information theory, this is the ideal introductory textbook for senior undergraduate and graduate students in electrical engineering, statistics, and computer science.
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Information and Coding Theory
As this Preface is being written, the twentieth century is coming to an end.Historians may perhaps come to refer to it as the century of information, just as its predecessor is associated with the process of industrialisation.Successive technological developments such as the telephone, radio, television, computers and the Internet have had profound effects on the way we live.We can see pic tures of the surface of Mars or the early shape of the Universe.The contents of a whole shelf-load of library books can be compressed onto an almost weight less piece of plastic.Billions of people can watch the same football match, or can keep in instant touch with friends around the world without leaving home.In short, massive amounts of information can now be stored, transmitted and processed, with surprising speed, accuracy and economy.Of course, these developments do not happen without some theoretical ba sis, and as is so often the case, much of this is provided by mathematics.Many of the first mathematical advances in this area were made in the mid-twentieth century by engineers, often relying on intuition and experience rather than a deep theoretical knowledge to lead them to their discoveries.Soon the math ematicians, delighted to see new applications for their subject, joined in and developed the engineers' practical examples into wide-ranging theories, com plete with definitions, theorems and proofs.
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Programming Machine Learning : From Coding to Deep Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool.But where to start? It's easy to be intimidated, even as a software developer.The good news is that it doesn't have to be that hard.Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system.Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty.Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning.Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work.Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on.Iterate on your design, and add layers of complexity as you go.Build an image recognition application from scratch with supervised learning.Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning.Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.Train and refine those networks with backpropagation and batching.Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.Start from the beginning and code your way to machine learning mastery.What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly.Apart from that, you'll only need your computer, and your code-adept brain.
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Food Information, Communication and Education : Eating Knowledge
Food Information, Communication and Education analyses the role of different media in producing and transforming knowledge about food. ‘Eating knowledge’, or knowledge about food and food practice, is a central theme of cooking classes, the daily press, school textbooks, social media, popular magazines and other media.In addition, a wide variety of actors have taken on the responsibility of informing and educating the public about food, including food producers, advertising agencies, celebrity chefs, teachers, food bloggers and government institutions. Featuring a range of European case studies, this interdisciplinary collection advances our understanding of the processes of mediatization, circulation and reception of knowledge relating to food within specific social environments.Topics covered include: popularized knowledge about food carried over from past to present; the construction of trustworthy knowledge in today’s food risk society; critical assessment of nutrition education initiatives for children; and political and ideological implications of food information policy and practice.
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What is the difference between the coding and non-coding strand?
The coding strand, also known as the sense strand, is the DNA strand that has the same sequence as the mRNA that is transcribed from it. This means that the coding strand has the same sequence as the resulting protein. On the other hand, the non-coding strand, also known as the template strand, is the DNA strand that is used as a template for the synthesis of mRNA during transcription. It has a complementary sequence to the mRNA and does not directly code for the protein.
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What is Huffman coding?
Huffman coding is a method used for lossless data compression, where variable-length codes are assigned to different characters based on their frequencies in the input data. This technique ensures that more frequent characters are assigned shorter codes, while less frequent characters are assigned longer codes, resulting in overall compression of the data. Huffman coding is widely used in various applications, such as file compression algorithms and data transmission protocols, to reduce the size of data without losing any information.
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How can one learn coding?
One can learn coding through a variety of resources such as online tutorials, coding bootcamps, and formal education programs. There are many websites and platforms that offer free or paid coding courses, such as Codecademy, Coursera, and Udemy. Additionally, joining coding communities and forums can provide valuable support and guidance from experienced programmers. It's also important to practice coding regularly and work on real-world projects to gain practical experience and improve skills.
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Are there any coding errors?
No, there are no coding errors in the provided text.
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DKfindout! Coding
Silver award winner in the MadeForMums Awards 2017 children's books series category. DKfindout! Coding explains coding clearly for children with beautiful photography, lively illustrations, and key curriculum information.The DKfindout! series will satisfy any child who is eager to learn and acquire facts - and keep them coming back for more!DKfindout!Coding is packed with up-to-date information and fun quizzes, while key curriculum text tells children why we use code, how computers use code and covers the history of coding from early computer programming languages to Python and Scratch.Learn about coding pioneers Ada Lovelace and Alan Turing and how codes have been used throughout history. DKfindout! Coding will surprise and delight young readers.
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Coding Android Apps
As Android apps continue to grow in popularity and an associated job market emerges, the ability to develop software and applications for Android smartphones will only grow more relevant in the foreseeable future.Compiled from materials used in over a decade of teaching undergraduate and graduate students majoring in computer science and information technology, this book is a hands-on, step-by-step guide to coding Android apps that have been rigorously tested. KEY FEATURESEach chapter begins with a list of student learning outcomes that can be used for assessment purposes and syllabus constructionThe mechanics of Android app creation is presented in a very detailed, step-by-step progression, with accompanying screenshots and code explanationsNew topics are introduced chapter-by-chapter in a very logical and gradational instructional mannerVery detailed exercises are provided at the end of each chapter and can be used for class activities and as homework assignments.Each chapter includes multiple exercises of varying difficultyVideo lessons are available as supplementary resources for each chapter to quickly illustrate in a demonstrative and visual manner the Java and XML code and Android Studio development actions covered in the chapterThis book is particularly appealing for students of mobile apps development courses offered in computer science and information technology departments, as well as information systems disciplines within business schools, at both the undergraduate and graduate levels.
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Coding with Scratch
Written by the founders of Silicon Valley’s the CoderSchool, Basher’s Coding With Scratch is a really useful step-by-step guide to basic programming that’s packed with quirky, colourful characters – from Variable and If/Then to Loop and Function – who will teach you how to make your very own apps with Scratch 3.0.Young readers will learn all the basics of programming, then put their knowledge to the test in a series of apps, before building their first actual computer game.Plus there are lots of fun challenges to try along the way!Combining Basher's trademark quirky and humorous illustration style with the very latest teachings on coding, Coding With Scratch is the ultimate step-by-step guide to mastering Scratch.
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Baby Loves Coding!
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I need help with coding.
Of course! I'd be happy to help with coding. Please let me know what specific coding issue you are facing or what you need assistance with, and I'll do my best to provide guidance or support. Whether it's troubleshooting errors, understanding concepts, or writing code from scratch, I'm here to assist you. Just provide me with more details about your coding needs, and we can work through it together.
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Is coding uncomfortable for you?
As an AI, I don't have physical discomfort, so coding is not uncomfortable for me. However, for humans, coding can be uncomfortable at times, especially when facing challenging problems or long periods of sitting and staring at a screen. It's important to take breaks, stretch, and practice good ergonomics to minimize discomfort while coding. Additionally, building up coding skills and experience can help make the process more comfortable and enjoyable.
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How can one best create a Minecraft mod, both without coding and with coding?
One way to create a Minecraft mod without coding is to use a modding platform or tool that provides a user-friendly interface for making changes to the game. These platforms often allow users to customize the game by adding new items, blocks, or mechanics without needing to write code. On the other hand, creating a Minecraft mod with coding involves learning the Java programming language, as Minecraft mods are typically written in Java. There are many resources available online, such as tutorials and forums, that can help beginners learn how to code Minecraft mods. Additionally, using a modding API like Forge or Fabric can simplify the process of creating mods by providing a framework and tools for developers to work with.
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What is the exact difference between source coding and channel coding in digital modulation?
Source coding and channel coding are two different processes in digital modulation. Source coding is the process of compressing the data to reduce the amount of information that needs to be transmitted. It aims to remove redundancy and irrelevant information from the source data. On the other hand, channel coding is the process of adding redundancy to the data before transmission in order to make it more resilient to errors that may occur during transmission. It aims to protect the data from noise and interference in the communication channel. In summary, source coding reduces the amount of data to be transmitted, while channel coding adds redundancy to protect the data during transmission.
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