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
Learning Rate Schedules and Adjustment
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook offering comprehensive coverage of deep learning principles, including optimization algorithms, gradient descent, and the rationale for dynamic learning rate adjustments.
- SGDR: Stochastic Gradient Descent with Warm Restarts, Ilya Loshchilov, Frank Hutter, 2017 International Conference on Learning Representations DOI: 10.48550/arXiv.1608.03983 - Introduces the concept of cosine annealing with warm restarts, a technique for learning rate scheduling that frequently achieves superior performance in deep learning training.
- A Disciplined Approach to Neural Network Hyper-parameters: Part 1 -- Learning Rate, Batch Size, Momentum, and Weight Decay, Leslie N. Smith, 2018 arXiv preprint arXiv:1803.09820 (US Naval Research Laboratory) DOI: 10.48550/arXiv.1803.09820 - Offers practical guidelines for setting neural network hyperparameters, featuring systematic strategies for learning rate scheduling, including the One-Cycle Policy which incorporates warmup and annealing.
- How to adjust learning rate, PyTorch Documentation, 2023 (PyTorch) - Official documentation providing practical guidance and examples for implementing various learning rate schedulers within the PyTorch deep learning framework.