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Image Processing with Keras in Python

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

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4 Hours13 Videos45 Exercises29,667 Learners3650 XPImage Processing TrackMachine Learning Scientist Track

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Course Description

Learn to Use Convolutional Neural Networks in Python

Image processing 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 which 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.
  1. 1

    Image Processing With Neural Networks


    Convolutional neural networks use the data that is represented in images to learn. In this chapter, we will probe data in images, and we will learn how to use Keras to train a neural network to classify objects that appear in images.

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    Introducing convolutional neural networks
    50 xp
    Images as data: visualizations
    100 xp
    Images as data: changing images
    100 xp
    Classifying images
    50 xp
    Using one-hot encoding to represent images
    100 xp
    Evaluating a classifier
    100 xp
    Classification with Keras
    50 xp
    Build a neural network
    100 xp
    Compile a neural network
    100 xp
    Fitting a neural network model to clothing data
    100 xp
    Cross-validation for neural network evaluation
    100 xp
  2. 3

    Going Deeper

    Convolutional neural networks gain a lot of power when they are constructed with multiple layers (deep networks). In this chapter, you will learn how to stack multiple convolutional layers into a deep network. You will also learn how to keep track of the number of parameters, as the network grows, and how to control this number.

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  3. 4

    Understanding and Improving Deep Convolutional Networks

    There are many ways to improve training by neural networks. In this chapter, we will focus on our ability to track how well a network is doing, and explore approaches towards improving convolutional neural networks.

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In the following tracks

Image ProcessingMachine Learning Scientist


Sumedh Panchadhar
Lore Dirick
Eunkyung Park
Ariel Rokem Headshot

Ariel Rokem

Senior Data Scientist, University of Washington

Ariel Rokem is a Data Scientist at the University of Washington eScience Institute. He received a PhD in neuroscience from UC Berkeley, and postdoctoral training in computational neuroimaging at Stanford. In his work, he develops data science algorithms and tools, and applies them to analysis of neural data. He is also a contributor to multiple open-source software projects in the scientific Python ecosystem.
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