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DEPARTAMENTOS
INTERESSE GERAL
INTERESSE GERAL 2
LIVROS TÉCNICOS
LIVROS TÉCNICOS 2
CATEGORIAS
- IMPORTADOS
Codeless Deep Learning with KNIME
Cód:
491_9781800566613
De: R$ 0,00Por: R$ 444,50ou X de
Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutionsKey FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME GUIDiscover different deployment options without using a single line of code with KNIME Analytics PlatformBook DescriptionKNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. Itll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems.Starting with an introduction to KNIME Analytics Platform, youll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. Youll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, youll learn how to prepare data, encode incoming data, and apply best practices.By the end of this book, youll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.What You Will LearnUse various common nodes to transform your data into the right structure suitable for training a neural networkUnderstand neural network techniques such as loss functions, backpropagation, and hyperparametersPrepare and encode data appropriately to feed it into the networkBuild and train a classic feedforward networkDevelop and optimize an autoencoder network for outlier detectionImplement deep learning networks such as CNNs, RNNs, and LSTM with the help of pra
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