ISBN: 9781788397872 - neural networks with r: smart models using cnn, rnn, deep learning, and artificial intelligence principles: build smart systems by implementing popular deep learning models in r (23 resultados)

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Digital. Condición: New. Uncover the power of artificial neural networks by implementing them through R code.About This Book. Develop a strong background in neural networks with R, to implement them in your applications. Build smart systems using the power of deep learning. Real-world case studies to illustrate the power of neural network modelsWho This Book Is ForThis book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!What You Will Learn. Set up R packages for neural networks and deep learning. Understand the core concepts of artificial neural networks. Understand neurons, perceptrons, bias, weights, and activation functions. Implement supervised and unsupervised machine learning in R for neural networks. Predict and classify data automatically using neural networks. Evaluate and fine-tune the models you build.In DetailNeural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.Style and approachA step-by-step guide filled with real-world practical examples.…

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Digital. Condición: New. Uncover the power of artificial neural networks by implementing them through R code.About This Book. Develop a strong background in neural networks with R, to implement them in your applications. Build smart systems using the power of deep learning. Real-world case studies to illustrate the power of neural network modelsWho This Book Is ForThis book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!What You Will Learn. Set up R packages for neural networks and deep learning. Understand the core concepts of artificial neural networks. Understand neurons, perceptrons, bias, weights, and activation functions. Implement supervised and unsupervised machine learning in R for neural networks. Predict and classify data automatically using neural networks. Evaluate and fine-tune the models you build.In DetailNeural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.Style and approachA step-by-step guide filled with real-world practical examples.…

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorrnrnBalaji Venkateswaran is an AI expert, data scientist, machine learning practitioner, and database architect. He has 17+ years of experience in investment banking payment processing, telecom billing, and project management.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guideKey FeaturesGet started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.Book DescriptionAs the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.What You Will LearnAcquaint yourself with important elements of Machine LearningUnderstand the feature selection and feature engineering processAssess performance and error trade-offs for Linear RegressionBuild a data model and understand how it works by using different types of algorithmLearn to tune the parameters of Support Vector machinesImplement clusters to a datasetExplore the concept of Natural Processing Language and Recommendation SystemsCreate a ML architecture from scratch.Who This Book Is ForThis book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.…

Taschenbuch. Condición: Neu. Neural Networks with R | Build smart systems by implementing popular deep learning models in R | Balaji Venkateswaran (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2017 | Packt Publishing | EAN 9781788397872 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …