Generative AI Models: Generating Data Using Variational Autoencoders
Master VAEs for image generation: Learn probabilistic encoders and decoders. Train models on multichannel color images using Python in Colab.
Skill level
IntermediateTime to complete
Approx. 1 hourCertificate of completion
Yes
About this course
Variational autoencoders (VAEs) represent a powerful variant of traditional autoencoders, designed to address the challenge of generating new and diverse samples from the learned latent space. VAEs introduce probabilistic components, incorporating a probabilistic encoder that maps input data to a distribution in the latent space and a decoder that reconstructs data from samples drawn from this distribution. Begin this course by discovering how variational autoencoders can be used for generating images. Next, you will create and train VAEs in Python and the Google Colab environment. Then you will construct the encoder and decoder. Finally, you will train the VAE on multichannel color images. Upon course completion, you will have a solid understanding of variational autoencoders and their use in generating images.
Learning objectives
- Discover the key concepts covered in this course
- Provide an overview of variational autoencoders (vaes)
- Describe the architecture of vaes
How it works
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