Learn to Use Convolutional Neural Networks in Python
Image model often requires deep learning methods that use data to train neural
network algorithms to do various machine learning tasks. Convolutional neural
networks (CNNs) are particularly powerful neural networks that you’ll use to
classify different types of objects for the analysis of images. This four-hour
course will teach you how to construct, train, and evaluate CNNs using Keras.

Turning images into data and teaching neural networks to classify them is a
challenging element of deep learning with extensive applications throughout
business and research, from helping an eCommerce site manage inventory more
easily to allowing cancer researchers to quickly spot dangerous melanoma.

Discover Keras CNNs
The first chapter of this course covers how images can be seen as data, and
how you can use Keras to train a neural network to classify objects found in
images.

The second chapter will cover convolutions, a fundamental part of CNNs. You’ll
learn how they operate on image data and learn how to train and tweak your
Keras CNN using test data. Later chapters go into more detail and teach you
how to create a deep learning network.

Build Your Own Keras Neural Network
You’ll end the course by learning the different ways that you can track how
well a CNN is doing and how you can improve their performance. At this point,
you’ll be able to build Keras neural networks, optimize them, and visualize
their responses across a range of applications.