Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Learning Data Mining with Python
  • Toc
  • feedback
Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
close
Learning Data Mining with Python

Learning Data Mining with Python

By: Robert Layton

Overview of this book

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.
Table of Contents (14 chapters)
close

Using Keras


TensorFlow is not a library to directly build neural networks. In a similar way, NumPy is not a library to perform data mining; it just does the heavy lifting and is generally used from another library. TensorFlow contains a built-in library, referred to as TensorFlow Learn to build networks and perform data mining. Other libraries, such as Keras, are also built with this in mind and use TensorFlow in the backend.

Keras implements a number of modern types of neural network layers and the building blocks for building them. In this chapter, we will use convolution layers which are designed to mimic the way in which human vision works. They use small collections of connected neurons that analyse only a segment of the input values - in this case, an image. This allows the network to deal with standard alterations such as dealing with translations of images. In the case of vision-based experiments, an example of an alteration dealt with by convolution layers is translating the image...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete