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Image Modeling with Keras

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

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4 Hours13 Videos45 Exercises
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Course Description

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.
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In the following Tracks

Image Processing with Python

Go To Track

Keras Fundamentals

Go To Track
  1. 1

    Image Processing With Neural Networks

    Free

    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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GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Image Processing with Python

Go To Track

Keras Fundamentals

Go To Track

Collaborators

Collaborator's avatar
Lore Dirick
Collaborator's avatar
Eunkyung Park
Collaborator's avatar
Sumedh Panchadhar

Prerequisites

Introduction to Deep Learning in Python
Ariel Rokem HeadshotAriel Rokem

Senior Data Scientist, University of Washington

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Don’t just take our word for it

*4.2
from 15 reviews
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  • Gustavo T.
    13 days

    Good explanation, clear examples. Step by step solution solving is good

  • Jere S.
    about 1 month

    great course

  • Federico C.
    3 months

    Really nice course

  • tinh n.
    7 months

    I am very happy to study this course

  • Albert T.
    about 1 year

    Exceptionally structured clear education.

"Good explanation, clear examples. Step by step solution solving is good"

Gustavo T.

"great course"

Jere S.

"Really nice course"

Federico C.

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