Generative AI Models: Getting Started with Autoencoders
Dive into unsupervised learning with autoencoders. Train models to reconstruct high-dimensional images and denoise corrupted images using PyTorch in colab.
Skill level
IntermediateTime to complete
Approx. 2 hoursCertificate of completion
Yes
About this course
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.
Learning objectives
- Discover the key concepts covered in this course
- Recall how autoencoders work
- Provide an overview of the autoencoder architecture
How it works
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You've Heard of Generative AI, But What Else Is Out There?
In the past year, our understanding of AI has increased as programs like ChatGPT, DALL-E, and Midjourney have become ubiquitous tools in our daily lives. These programs all fall under the same category of generative AI, a type of AI that excels at synthesizing new text and media content. But AI can do much more than generate content, and it’s worth exploring and familiarizing yourself with the technology’s full range of possibilities.
Before we get into the different types of AI, it helps to define what AI is. People often use AI as an umbrella term to describe many different techniques, says Nitya Mandyam, Codecademy Senior Curriculum Developer. (Fun fact: Along with creating a ton of our data science courses and content, Nitya’s also pursuing a postgraduate degree in AI Ethics.)
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