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Codeless Deep Learning with KNIME
Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME: Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

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Profile Icon KNIME AG Profile Icon Melcher Profile Icon Rosaria Silipo
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$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (10 Ratings)
Paperback Nov 2020 384 pages 1st Edition
eBook
$38.99
Paperback
$54.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon KNIME AG Profile Icon Melcher Profile Icon Rosaria Silipo
Arrow right icon
$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (10 Ratings)
Paperback Nov 2020 384 pages 1st Edition
eBook
$38.99
Paperback
$54.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$38.99
Paperback
$54.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • Become well-versed with KNIME Analytics Platform to perform codeless deep learning
  • Design and build deep learning workflows quickly and more easily using the KNIME GUI
  • Discover different deployment options without using a single line of code with KNIME Analytics Platform

Description

KNIME 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. It’ll 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, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll 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, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.

Who is this book for?

This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.

What you will learn

  • Use various common nodes to transform your data into the right structure suitable for training a neural network
  • Understand neural network techniques such as loss functions, backpropagation, and hyperparameters
  • Prepare and encode data appropriately to feed it into the network
  • Build and train a classic feedforward network
  • Develop and optimize an autoencoder network for outlier detection
  • Implement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examples
  • Deploy a trained deep learning network on real-world data

Product Details

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Publication date : Nov 27, 2020
Length: 384 pages
Edition : 1st
Language : English
ISBN-13 : 9781800566613
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Product Details

Publication date : Nov 27, 2020
Length: 384 pages
Edition : 1st
Language : English
ISBN-13 : 9781800566613
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Languages :
Concepts :
Tools :

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Frequently bought together


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Joe Porter Jan 24, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
KNIME continues to Impress! Since I first discovered KNIME 8-10 years ago I have been impressed by it’s ease of use and ability to quickly give analytically oriented people the ability to perform complex data engineering and data science tasks at scale.In this book, KNIME continues this tradition as it does a great job of teaching us multiple deep learning methods with real world use-cases.I would highly recommend this book and the KNIME software to anyone interested in Data Science but wants an alternative to code.
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Stefano Jan 30, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very useful, it help for both to learn NN and to develop without codino! It's the base to democratize data
Amazon Verified review Amazon
Vijaykrishna Venkataraman Dec 07, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book makes deep learning accessible to anyone with little or no prior programming experience. The user-friendly GUI integrations to the open-source KNIME Analytics Platform are built on robust and powerful deep learning frameworks like Keras and TensorFlow. Leverage on the shoulder of giants who already developed the tool and dive straight into trying your hands on some case studies without the burden/barrier of code. The authors provide the right mix of theoretical foundation followed by practical case studies on fraud detection, natural language processing (NLP), image classification, etc. The last section (two dedicated chapters) covers usually neglected topics, like implementing trained models to production and a much-needed chapter on deployment best practices. The deployment options include a web app or a REST web-service again without any coding in no time; however, it requires access to a licensed version of the KNIME Server.To summarize, if you learn best by doing, you can't go wrong with this book. I recommend this to someone who wants to get started but may feel a little lost and anxious. You'll want to check out the book's companion workflows at KNIME Hub. It contains lots of workflows with configured nodes and data that you can run right on your computer.
Amazon Verified review Amazon
Themos Kalafatis Dec 07, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This title seemed interesting for two reasons : KNIME is one of the most respected software frameworks for Data Science projects and Deep Learning is one of the hottest Machine Learning techniques with many successful real-world applications.The important point to keep in mind is that the book is written for readers that do not know how to code. Because of this there are no examples for one important functionality of KNIME which is the use of a programming language such as Java, R or Python to pre-process and analyze data.The book walks the reader through KNIME and its key processes with many examples and content related to Deep Learning theory. After the reader learns about the basics of the KNIME framework and the theory of Deep Learning, several sections provide practical examples with the aim of putting everything together.Sections of the practical applications include Natural Language Processing, Image Analysis, Machine translation, Fraud Detection and Energy Demand Prediction. For each application the authors provide information on the necessary steps of pre-processing, analyzing and testing. Finally, a special section is given for the Deployment phase of a project which is often not well explained in many Data Science books.The pros :1) Well-written book, explains key concepts and gives best practices2) Many practical applications provided3) Extensive information on Deployment options.The cons :1) I would prefer an example of Natural Language Processing such as Entity Detection (a technique that transforms unstructured data to a structured format) to be given instead of the Free text generation example2) Although the KNIME framework has so many of them, it would be nice if the authors simply mentioned -rather than fully explain due to limited space- more KNIME Nodes that are likely to be of use when it comes to accessing and pre-processing data. This addition could prove very useful since with programming a lot of pre-processing actions can be implemented and programming is not part of this book.Overall, a very useful and well-written book.
Amazon Verified review Amazon
Ema Feb 07, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very interesting book with useful examples and descriptions. Hope to see more books like this in the future!
Amazon Verified review Amazon
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