Products related to Processing:
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Learning Processing : A Beginner's Guide to Programming Images, Animation, and Interaction
Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages.Requiring no previous experience, this book is for the true programming beginner.It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization.Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve. A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques.The book has been developed with a supportive learning experience at its core.From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. This book is ideal for graphic designers and visual artists without programming background who want to learn programming.It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study.
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Natural Language Processing for Software Engineering
Discover how Natural Language Processing for Software Engineering can transform your understanding of agile development, equipping you with essential tools and insights to enhance software quality and responsiveness in today’s rapidly changing technological landscape.Agile development enhances business responsiveness through continuous software delivery, emphasizing iterative methodologies that produce incremental, usable software.Working software is the main measure of progress, and ongoing customer collaboration is essential.Approaches like Scrum, eXtreme Programming (XP), and Crystal share these principles but differ in focus: Scrum reduces documentation, XP improves software quality and adaptability to changing requirements, and Crystal emphasizes people and interactions while retaining key artifacts.Modifying software systems designed with Object-Oriented Analysis and Design can be costly and time-consuming in rapidly changing environments requiring frequent updates.This book explores how natural language processing can enhance agile methodologies, particularly in requirements engineering.It introduces tools that help developers create, organize, and update documentation throughout the agile project process.
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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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Deep Learning for Natural Language Processing
Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context.Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively.In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning * Models for textual similarity * Deep memory-based NLP * Semantic role labeling * Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP.No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language.Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics.He's the technical coordinator of two large European Union-funded research security-related projects.He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.
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Is learning programming and software development very challenging?
Learning programming and software development can be challenging for some people, as it requires logical thinking, problem-solving skills, and attention to detail. However, with dedication, practice, and the right resources, it is definitely achievable. Breaking down complex concepts into smaller, more manageable parts and seeking help from online tutorials, courses, and communities can make the learning process easier and more enjoyable. Ultimately, the level of challenge will vary depending on the individual's background, experience, and learning style.
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What is the Processing programming language?
Processing is a flexible software sketchbook and a language for learning how to code within the context of the visual arts. It was created to teach fundamentals of computer programming within a visual context and to serve as a software sketchbook and professional production tool. Processing is an open-source programming language and environment for people who want to create images, animations, and interactions. It is widely used by artists, designers, and students to create visual and interactive art and design projects.
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How can one learn programming with Processing?
One can learn programming with Processing by starting with the basics of the language and syntax. There are many online tutorials, courses, and resources available that cater to beginners. It is also helpful to practice coding regularly and experiment with different features and functions within Processing. Additionally, joining online communities or forums can provide support and guidance from experienced programmers.
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Can you help me with programming in Processing?
Yes, I can help you with programming in Processing. Processing is a flexible software sketchbook and a language for learning how to code within the context of the visual arts. I can assist you with understanding the basics of programming in Processing, creating visual and interactive projects, and troubleshooting any issues you may encounter. Feel free to ask me any specific questions you have about programming in Processing, and I'll do my best to help you.
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Machine Learning and Deep Learning in Natural Language Processing
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken.NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then they utilize algorithms to extract meaning and produce results.Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the NLP field, in particular about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content – like emotions, but also satirical expressions – and applications in the healthcare field. NLP has the potential to be a disruptive technology in various healthcare fields, but so far little attention has been devoted to that goal.This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson’s disease, or help psychotherapists. This book is intended for a wide audience. Beginners will find useful chapters providing a general introduction to NLP techniques, while experienced professionals will appreciate the chapters about advanced management of emotion, empathy, and non-literal content.
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Natural Language Processing : A Machine Learning Perspective
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework.NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems.Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods.Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own.Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
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Processing : A Programming Handbook for Visual Designers and Artists
The new edition of an introduction to computer programming within the context of the visual arts, using the open-source programming language Processing; thoroughly updated throughout.The visual arts are rapidly changing as media moves into the web, mobile devices, and architecture.When designers and artists learn the basics of writing software, they develop a new form of literacy that enables them to create new media for the present, and to imagine future media that are beyond the capacities of current software tools.This book introduces this new literacy by teaching computer programming within the context of the visual arts.It offers a comprehensive reference and text for Processing (www.processing.org), an open-source programming language that can be used by students, artists, designers, architects, researchers, and anyone who wants to program images, animation, and interactivity.Written by Processing's cofounders, the book offers a definitive reference for students and professionals.Tutorial chapters make up the bulk of the book; advanced professional projects from such domains as animation, performance, and installation are discussed in interviews with their creators. This second edition has been thoroughly updated. It is the first book to offer in-depth coverage of Processing 2.0 and 3.0, and all examples have been updated for the new syntax.Every chapter has been revised, and new chapters introduce new ways to work with data and geometry.New "synthesis" chapters offer discussion and worked examples of such topics as sketching with code, modularity, and algorithms.New interviews have been added that cover a wider range of projects. "Extension" chapters are now offered online so they can be updated to keep pace with technological developments in such fields as computer vision and electronics. InterviewsSUE.C, Larry Cuba, Mark Hansen, Lynn Hershman Leeson, Jurg Lehni, LettError, Golan Levin and Zachary Lieberman, Benjamin Maus, Manfred Mohr, Ash Nehru, Josh On, Bob Sabiston, Jennifer Steinkamp, Jared Tarbell, Steph Thirion, Robert Winter
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Signal Processing and Machine Learning with Applications
Signal processing captures, interprets, describes and manipulates physical phenomena.Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information.This book presents different kinds of signals humans use and applies them for human machine interaction to communicate.Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications.It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence.The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes.The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engagedwith signal processing, machine learning and the applications.
Price: 44.99 £ | Shipping*: 0.00 £
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Which programming language is well-suited for processing data?
Python is well-suited for processing data due to its extensive libraries and tools for data analysis, manipulation, and visualization. Libraries such as Pandas, NumPy, and Matplotlib make it easy to work with large datasets and perform complex data operations. Additionally, Python's simplicity and readability make it a popular choice for data processing tasks, as it allows for quick and efficient development of data-related applications.
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Which programming languages are used in software development?
There are many programming languages used in software development, including popular languages such as Java, Python, C++, JavaScript, and Ruby. Each language has its own strengths and is used for different purposes in software development. For example, Java is commonly used for building enterprise-level applications, while Python is known for its simplicity and versatility. C++ is often used for system software and game development, while JavaScript is essential for web development. Overall, the choice of programming language depends on the specific requirements of the software being developed.
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Is working memory processing speed good for learning in school/studies?
Yes, working memory processing speed is important for learning in school/studies. It allows individuals to quickly take in information, process it, and retain it for further use. A faster processing speed can help students keep up with the pace of lessons, understand complex concepts more easily, and perform better on tasks that require quick thinking and problem-solving. Overall, a strong working memory processing speed can enhance academic performance and make learning more efficient.
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What is the difference between software development and programming?
Software development is a broader term that encompasses the entire process of creating software, including planning, designing, testing, and maintaining software applications. Programming, on the other hand, refers specifically to the act of writing code to instruct a computer to perform certain tasks. While programming is a key component of software development, software development involves a more comprehensive approach that includes various stages beyond just writing code.
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