Codeless Deep Learning with KNIME: Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform (無需編碼的深度學習:使用 KNIME 分析平台構建、訓練和部署各種深度神經網絡架構)
Melcher, Kathrin, Silipo, Rosaria
- 出版商: Packt Publishing
- 出版日期: 2020-11-27
- 售價: $2,180
- 貴賓價: 9.5 折 $2,071
- 語言: 英文
- 頁數: 408
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1800566611
- ISBN-13: 9781800566613
-
相關分類:
DeepLearning
海外代購書籍(需單獨結帳)
買這商品的人也買了...
-
$680$537 -
$390$332 -
$450$356 -
$500$390 -
$440$374 -
$520$411 -
$2,655Deep Learning for the Life Sciences (Paperback)
-
$620$490 -
$332了不起的 Markdown
-
$1,650$1,568 -
$580$452 -
$1,780$1,691 -
$1,200$948
相關主題
商品描述
Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions
Key Features
- 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
Book 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.
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
Who this book is 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.
商品描述(中文翻譯)
本書將教導您如何將KNIME Analytics Platform與深度學習庫整合,以實現人工智慧解決方案。
主要特點:
- 熟悉KNIME Analytics Platform,進行無代碼深度學習
- 使用KNIME GUI快速且更輕鬆地設計和構建深度學習工作流程
- 在KNIME Analytics Platform中,探索不使用任何代碼的不同部署選項
書籍描述:
KNIME Analytics Platform是一款用於創建和設計數據科學工作流程的開源軟件。本書是KNIME GUI和KNIME深度學習整合的全面指南,幫助您構建神經網絡模型而無需編寫任何代碼。它將通過實際和創造性的解決方案,引導您構建簡單和複雜的神經網絡,解決現實世界的數據問題。
從介紹KNIME Analytics Platform開始,您將瞭解在相對較小的數據集上解決簡單分類問題的簡單前饋網絡。然後,您將進一步構建、訓練、測試和部署更複雜的網絡,例如自編碼器、循環神經網絡(RNN)、長短期記憶(LSTM)和卷積神經網絡(CNN)。在每個章節中,根據網絡和使用案例的不同,您將學習如何準備數據、編碼輸入數據並應用最佳實踐。
通過閱讀本書,您將學習如何設計各種不同的神經結構,並能夠訓練、測試和部署最終的網絡。
您將學到:
- 使用各種常見節點將數據轉換為適合訓練神經網絡的正確結構
- 瞭解神經網絡技術,如損失函數、反向傳播和超參數
- 適當地準備和編碼數據以供網絡使用
- 構建和訓練經典的前饋網絡
- 開發和優化用於異常檢測的自編碼器網絡
- 使用實際示例實現卷積神經網絡(CNN)、循環神經網絡(RNN)和長短期記憶(LSTM)的深度學習網絡
- 在真實數據上部署訓練好的深度學習網絡
本書適合數據分析師、數據科學家和深度學習開發人員,他們對Python不熟悉,但希望學習如何使用KNIME GUI構建、訓練、測試和部署具有不同結構的神經網絡。本書中展示的實際實現不需要編碼或任何專用腳本的知識,因此您可以輕鬆將所學應用於實際應用中。開始閱讀本書無需先前使用KNIME的經驗。