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

  • Time to complete

    Approx. 2 hours
  • Certificate of completion


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

Expert-led videos

In this course, you'll watch videos created by industry-leading experts for some of the biggest tech companies in the world. They'll cover key concepts, go through sample applications, prepare you for industry certifications, and more. Watch on any device — whenever and wherever you want — to learn at your own pace.

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You've Heard of Generative AI, But What Else Is Out There? 

Jacob Johnson
Nov 9, 2023

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.)  

“AI is an interesting field because it’s kind of defined by the end goal, which is achieving human or superhuman intelligence, rather than what it actually is,” Nitya says. “Curiously, this lack of a precise universally accepted definition has probably helped the field grow and blossom.” Simply put, AI refers to intentionally constructed systems that actively interact with the world and are usually guided by large data models.

Early AI systems were inspired by mathematical logic and sought to mimic conscious thought processes through rules. (For example, in the late 90s, IBM built a chess-playing commputer called Deep Blue that was able to beat a grandmaster in chess.) Today, the many different types of AI that we encounter are much more sophisticated, in part because our understanding of human intelligence is more in-depth, Nitya explains. 

While its roots go back decades, modern AI really accelerated in the last few years. As AI advances and seeps into technology we use, the lines between different types of AI can be hard to distinguish. Ahead, Nitya breaks down the various categories of AI that you might come across and key differences that you should know about each. 

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