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
Simple Visualization of Learned Representations
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Comprehensive coverage of autoencoders, their architecture, and the properties of learned representations. It addresses concepts of latent space and feature learning central to this section.
- Reducing the Dimensionality of Data with Neural Networks, Geoffrey E. Hinton, Ruslan R. Salakhutdinov, 2006 Science, Vol. 313 DOI: 10.1126/science.1127647 - Presents the deep autoencoder concept for dimensionality reduction, a core mechanism for generating the low-dimensional latent spaces discussed in this section.
- Visualizing Data using t-SNE, Laurens van der Maaten, Geoffrey Hinton, 2008 Journal of Machine Learning Research, Vol. 9 - Introduces t-SNE, a technique for visualizing high-dimensional data. This method is directly applicable to projecting higher-dimensional autoencoder latent spaces into 2D or 3D for inspection.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide including implementation examples for autoencoders, with demonstrations of latent space projections and reconstructions.