This course introduces autoencoders, a type of artificial neural network used for unsupervised learning, and their application in feature learning. You will learn the fundamental architecture of autoencoders, how they compress and reconstruct data, and their role in dimensionality reduction and data representation. The course covers core concepts with practical examples to build your understanding from the ground up.
Prerequisites Basic Python knowledge
Level:
Autoencoder Fundamentals
Understand the basic architecture and purpose of an autoencoder.
Encoding and Decoding
Explain the process of data compression (encoding) and reconstruction (decoding) in autoencoders.
Feature Learning
Describe how autoencoders can be used to learn meaningful representations (features) from data.
Dimensionality Reduction
Grasp how autoencoders perform dimensionality reduction.
Basic Implementation
Implement a simple autoencoder using a common machine learning library.