Introduction to Autoencoders and Feature Learning
Chapter 1: Understanding Autoencoders
Introduction to Unsupervised Learning
A Brief Overview of Neural Networks
The Core Idea: Learning Data Reconstruction
Encoder, Bottleneck, and Decoder: The Main Parts
The Purpose of Learning Data Representations
An Autoencoder Analogy
Initial Problems Addressed by Autoencoders
Quiz for Chapter 1
Chapter 2: Anatomy of an Autoencoder: Encoder and Decoder
The Encoder: Compressing Data
Structure of the Input Layer
Encoder Hidden Layers and Data Compression
The Bottleneck: The Compact Representation
Common Activation Functions in Encoders
The Decoder: Reconstructing Data
Decoder Hidden Layers and Data Decompression
Structure of the Output Layer
Common Activation Functions in Decoders
Matching Input to Output
Quiz for Chapter 2
Chapter 3: How Autoencoders Learn
Training Objective: Reducing Reconstruction Error
Loss Functions for Autoencoders (MSE, BCE)
The Learning Process: Optimization Basics
Data Flow: Forward Propagation Explained
Learning from Errors: Backpropagation (High-Level)
Training Cycles: Epochs and Batches
A Glimpse into Overfitting and Underfitting
Preparing to Build an Autoencoder
Quiz for Chapter 3
Chapter 4: Autoencoders and Learning Features
Defining Features within Datasets
Comparing Manual and Learned Feature Approaches
How Autoencoders Identify Underlying Features
The Bottleneck Layer as a Feature Extractor
Reducing Dimensions with Autoencoders
Simple Visualization of Learned Representations
Importance of Effective Data Representations
Quiz for Chapter 4
Chapter 5: Building a Basic Autoencoder
Python Environment Setup for Deep Learning
Getting Started with PyTorch and Keras
Loading and Understanding a Basic Dataset
Data Preprocessing for Autoencoders
Constructing a Simple Autoencoder Model
Configuring the Model for Training
Executing the Training Process
Assessing Reconstruction Quality
Visualizing Reconstructed Outputs: Hands-on Practical
Examining Encoded Data: Practice
Quiz for Chapter 5
The Core Idea: Learning Data Reconstruction
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Provides a comprehensive introduction to autoencoders, detailing their architecture, the concept of a bottleneck, and the objective of learning efficient data representations through reconstruction.
- Reducing the Dimensionality of Data with Neural Networks, Geoffrey E. Hinton, Ruslan R. Salakhutdinov, 2006 Science, Vol. 313 (American Association for the Advancement of Science) DOI: 10.1126/science.1127647 - Presents a method for training deep autoencoders to reduce data dimensionality and learn compact, useful representations, central to the core idea of autoencoders.
- Extracting and Composing Robust Features with Denoising Autoencoders, Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol, 2008 Proceedings of the 25th International Conference on Machine Learning (ICML) (ACM) DOI: 10.1145/1390156.1390294 - Introduces denoising autoencoders, explaining how the reconstruction task forces the model to learn robust and meaningful features in the latent space, supporting the core idea.
- Autoencoders and Variational Autoencoders (CS230 Lecture Notes), Stanford University CS230 Staff, 2019 (Stanford University) - Offers clear explanations of autoencoder fundamentals, including their architecture, objective, and the role of the latent space for learning efficient data representations.