Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Codeless Deep Learning with KNIME
  • Table Of Contents Toc
Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

By : KNIME AG , Melcher, Rosaria Silipo
4.5 (10)
close
close
Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

4.5 (10)
By: KNIME AG , Melcher, Rosaria Silipo

Overview of this book

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.
Table of Contents (16 chapters)
close
close
1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

Summary

We have reached the end of this chapter, where we have learned the basic theoretical concepts behind neural networks and deep learning networks. All of this will be helpful to understand the steps for the practical implementation of deep learning networks described in the coming chapters.

We started with the artificial neuron and moved on to describe how to assemble and train a network of neurons, a fully connected feedforward neural network, via a variant of the gradient descent algorithm, using the backpropagation algorithm to calculate the gradient.

We concluded the chapter with a few hints on how to design and train a neural network. First, we described some commonly used network topologies, neural layers, and activation functions to design the appropriate neural architecture.

We then moved to analyze the effects of some parameters involved in the training algorithm. We introduced a few more parameters and techniques to optimize the training algorithm against...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Codeless Deep Learning with KNIME
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon