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
Visualizing Latent Spaces with t-SNE and UMAP
Was this section helpful?
- Visualizing Data using t-SNE, Laurens van der Maaten, Geoffrey Hinton, 2008 Journal of Machine Learning Research, Vol. 9 - Original research paper introducing t-SNE, detailing its mathematical formulation and applications.
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, Leland McInnes, John Healy, Nathaniel Saul, Lukas Großberger, 2018 Journal of Open Source Software, Vol. 3 (The Open Journal) DOI: 10.21105/joss.00861 - The seminal paper presenting UMAP, explaining its theoretical foundations and practical advantages.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - A comprehensive textbook covering foundational concepts of deep learning, including autoencoders, representation learning, and dimensionality reduction techniques.
- UMAP: Uniform Manifold Approximation and Projection - How UMAP Works, Leland McInnes, John Healy, James Melville, 2018 - Conceptual explanation of UMAP's algorithm, serving as an accessible guide to its theory and implementation details.
sklearn.manifold.TSNE- scikit-learn documentation, scikit-learn developers, 2023 (scikit-learn) - Official documentation providing an overview of t-SNE parameters, usage, and considerations within the scikit-learn library.