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
Introduction to Manifold Learning Techniques
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- Visualizing Data using t-SNE, Laurens van der Maaten and Geoffrey Hinton, 2008 Journal of Machine Learning Research, Vol. 9 DOI: 10.5555/1755833.1755834 - The original academic paper introducing t-Distributed Stochastic Neighbor Embedding (t-SNE), detailing its mathematical foundations and empirical performance for high-dimensional data visualization.
- 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 publication introducing UMAP, a modern manifold learning algorithm recognized for its efficiency and improved preservation of global data structure compared to t-SNE.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This widely-cited textbook includes a dedicated chapter (Chapter 15: Representation Learning) that discusses dimensionality reduction and manifold learning in the context of deep learning, providing essential background for autoencoders.
- UMAP: Uniform Manifold Approximation and Projection (Official Documentation), McInnes, L, Healy, J, 2024 - The official documentation for the UMAP Python library, offering practical guidance, detailed explanations of hyperparameters, and insights into its theoretical basis from the creators.