Generative AI Models: Getting Started with Autoencoders

Generative AI    |    Intermediate
  • 14 videos | 2h 15m 17s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 2 users Rating 4.0 of 2 users (2)
Autoencoders are a class of artificial neural networks employed in unsupervised learning tasks, primarily focused on data compression and feature learning. Begin this course off by exploring autoencoders, learning about the functions of the encoder and the decoder in the model. Next, you will learn how to create and train an autoencoder, using the Google Colab environment. Then you will use PyTorch to create the neural networks for the autoencoder, and you will train the model to reconstruct high-dimensional, grayscale images. You will also use convolutional autoencoders to work with multichannel color images. Finally, you will make use of the denoising autoencoder, a type of model that takes in a corrupted image with Gaussian noise, and attempts to reconstruct the original clean image, thus learning better representations of the input data. In conclusion, this course will provide you with a solid understanding of basic autoencoders and their use cases.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recall how autoencoders work
    Provide an overview of the autoencoder architecture
    Set up a google colab environment
    Import and view the fashion modified national institute of standards and technology (mnist) dataset
    Provide an overview of the autoencoder architecture
    Train the autoencoder and visualize reconstructions
  • Train convolutional autoencoders
    Reconstruct images with dense neural networks (dnns)
    Reconstruct images with convolutional neural networks (cnns)
    Describe the denoising autoencoder
    Reconstruct images with denoising autoencoders
    Provide an overview of the sparse autoencoder
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 2s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 12m 35s
    After completing this video, you will be able to recall how autoencoders work. FREE ACCESS
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    3.  Autoencoder Architectures
    14m 29s
    Upon completion of this video, you will be able to provide an overview of the autoencoder architecture. FREE ACCESS
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    4.  Setting up the Google Colab Environment
    10m 1s
    In this video, you will learn how to set up a Google Colab environment. FREE ACCESS
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    5.  Downloading and Exploring the Fashion MNIST Dataset
    9m 58s
    Find out how to import and view the Fashion Modified National Institute of Standards and Technology (MNIST) dataset. FREE ACCESS
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    6.  Configuring the Autoencoder Architecture
    7m 5s
    During this video, discover how to provide an overview of the autoencoder architecture. FREE ACCESS
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    7.  Training the Autoencoder and Visualizing Reconstructions
    11m 54s
    Learn how to train the autoencoder and visualize reconstructions. FREE ACCESS
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    8.  Training Convolutional Autoencoders
    13m 30s
    In this video, find out how to train convolutional autoencoders. FREE ACCESS
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    9.  Reconstructing Multichannel Images with DNN Autoencoders
    12m 4s
    Discover how to reconstruct images with dense neural networks (DNNs). FREE ACCESS
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    10.  Reconstructing Multichannel Images with CNN Autoencoders
    11m 25s
    During this video, you will learn how to reconstruct images with convolutional neural networks (CNNs). FREE ACCESS
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    11.  The Denoising Autoencoder
    5m 7s
    After completing this video, you will be able to describe the denoising autoencoder. FREE ACCESS
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    12.  Configuring and Training Denoising Autoencoders
    9m 52s
    Find out how to reconstruct images with denoising autoencoders. FREE ACCESS
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    13.  The Sparse Autoencoder
    12m 6s
    Upon completion of this video, you will be able to provide an overview of the sparse autoencoder. FREE ACCESS
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    14.  Course Summary
    3m 9s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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