Home Blog Learn

All Courses

Autoencoders and Representation Learning

Chapter 1: Foundations of Representation Learning

Review of Unsupervised Learning Principles

Limitations of Linear Dimensionality Reduction

Introduction to Manifold Learning Techniques

The Need for Non-linear Feature Extraction

Information Bottleneck Theory Primer

Mathematical Preliminaries Refresher

Chapter 2: The Classic Autoencoder Architecture

Encoder Network Design

The Bottleneck Layer

Decoder Network Design

Reconstruction Loss Functions

Mathematical Formulation of Basic Autoencoders

Implementation Considerations and Frameworks

Building a Simple Autoencoder: Hands-on Practical

Chapter 3: Regularized Autoencoders for Robust Representations

Addressing Overfitting in Autoencoders

Sparse Autoencoders: L1 and KL Divergence

Denoising Autoencoders Architecture and Training

Contractive Autoencoders Formulation

Comparison of Regularization Techniques

Implementing Denoising Autoencoders: Hands-on Practical

Implementing Sparse Autoencoders: Hands-on Practical

Chapter 4: Variational Autoencoders for Generative Modeling

Generative Limitations of Deterministic Autoencoders

Probabilistic Encoders and Decoders

The Latent Variable Model Perspective

The Reparameterization Trick Explained

Deriving the Evidence Lower Bound (ELBO)

KL Divergence Term Analysis

Reconstruction Loss Term in VAEs

Conditional Variational Autoencoders (CVAEs)

Implementing a VAE for Image Generation: Practice

Chapter 5: Advanced Autoencoder Architectures

Convolutional Autoencoders for Spatial Data

Recurrent Autoencoders for Sequential Data

Adversarial Autoencoders (AAEs)

Vector Quantized Variational Autoencoders (VQ-VAEs)

Transformer-Based Autoencoders Overview

Comparing Advanced Architectures

Implementing Convolutional Autoencoders: Practice

Chapter 6: Understanding and Manipulating Latent Spaces

Visualizing Latent Spaces with t-SNE and UMAP

Properties of Learned Representations

Disentangled Representations Theory

Techniques for Promoting Disentanglement

Interpolation and Traversal in Latent Space

Arithmetic Operations in Latent Space

Evaluating Representation Quality Metrics

Latent Space Visualization and Analysis: Hands-on Practical

Chapter 7: Applications and Training Strategies

Autoencoders for Anomaly Detection

Dimensionality Reduction and Data Compression Uses

Autoencoders for Pre-training Deep Networks

Image Denoising and Inpainting Applications

Sequence-to-Sequence Autoencoders Overview

Advanced Optimization Algorithms

Learning Rate Schedules and Adjustment

Hyperparameter Tuning Strategies

Implementing Anomaly Detection with Autoencoders: Practice

The Classic Autoencoder Architecture

Having established the context for representation learning, we now focus on the autoencoder. This chapter introduces the standard autoencoder architecture, a neural network used for unsupervised learning. The main idea is to first compress input data into a lower-dimensional representation (encoding) and then reconstruct the original data from that compression (decoding). This process helps the network learn significant features of the data.

In this chapter, you will examine the essential parts:

The Encoder Network: How it maps high-dimensional input $x$ to a latent code $z$ .
The Bottleneck Layer: Its function in holding the compressed data representation $z$ .
The Decoder Network: How it reconstructs the data $\hat{x}$ from the code $z$ .
Reconstruction Loss Functions: Measuring the discrepancy between $x$ and $\hat{x}$ , using functions like Mean Squared Error (MSE) $L_{MSE} = \frac{1}{N}\sum_{i=1}^{N}(x_i - \hat{x}_i)^2$ or Binary Cross-Entropy (BCE), depending on the input data characteristics.

We will also cover the mathematical definition of the basic autoencoder's objective function and discuss practical aspects related to implementation using current deep learning libraries. Finally, a hands-on practical section demonstrates building a simple autoencoder.

© 2025 ApX Machine Learning

;