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Deep Learning for Images with PyTorch

Apply PyTorch to images and use deep learning models for object detection with bounding boxes and image segmentation generation.

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4 Hours16 Videos58 Exercises
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Course Description

This course on deep learning for images using PyTorch will equip you with the practical skills and knowledge to excel in image classification, object detection, segmentation, and generation.

Classify images with convolutional neural networks (CNNs)

You'll apply CNNs for binary and multi-class image classification and understand how to leverage pre-trained models in PyTorch. With bounding boxes, you'll also be able to detect objects within an image and evaluate the performance of object recognition models.

Segment images by applying masks

Explore image segmentation, including semantic, instance, and panoptic segmentation, by applying masks to images and learn about the different model architectures needed for each type of segmentation.

Generate images with GANs

Finally, you'll learn how to generate your own images using Generative Adversarial Networks (GANs). You'll learn the skills to build and train Deep Convolutional GANs (DCGANs) and how to assess the quality and diversity of generated images. By the end of this course, you'll have gained the skills and experience to work with various image tasks using PyTorch models.
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In the following Tracks

Deep Learning in Python

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  1. 1

    Image classification with CNNs

    Free

    Learn about image classification with CNNs, the difference between the binary and multi-class image classification models, and how to use transfer learning for image classification in PyTorch.

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    Binary and multi-class image classification
    50 xp
    The number of classes
    50 xp
    Binary classification model
    100 xp
    Multi-class classification model
    100 xp
    Convolutional layers for images
    50 xp
    RGB, grayscale, or alpha?
    50 xp
    Adding a new convolutional layer
    100 xp
    Creating a sequential block
    100 xp
    Working with pre-trained models
    50 xp
    Save and load a model
    100 xp
    Loading a pre-trained model
    100 xp
    Image classification with ResNet
    100 xp
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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

Deep Learning in Python

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Collaborators

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James Chapman
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Jasmin Ludolf
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Olga Scrivner

Audio Recorded By

Michał Oleszak's avatar
Michał Oleszak

Prerequisites

Intermediate Deep Learning with PyTorch
Michał Oleszak HeadshotMichał Oleszak

Machine Learning Engineer

Michał is a Machine Learning Engineering Manager based in Zurich, Switzerland. He has a background in statistics and econometrics, holding an MSc degree from Erasmus University Rotterdam, The Netherlands. He has worn many hats, having worked at a consultancy, a start-up, a software house, and a large corporation. He blogs about anything machine learning. Visit his website to find out more.
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