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
Preparing to Build an Autoencoder
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook covering autoencoders, neural network architecture, training algorithms (backpropagation, gradient descent), data preprocessing, and concepts like feature learning and dimensionality reduction.
- CS230: Deep Learning, Andrew Ng, Kian Katanforoosh, 2019 - Provides comprehensive course materials and lecture notes on deep learning fundamentals, including neural network architecture, data preparation, training processes, and practical considerations, which are beneficial for autoencoder development.
- The Keras Sequential model, fchollet, 2023 (Keras) - Official Keras guide on building neural network models using the Sequential API, including defining layers and activation functions, directly relevant to constructing an autoencoder's architecture.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2019 (O'Reilly Media) - A practical guide that covers data preprocessing techniques (normalization, standardization, reshaping, splitting), neural network architecture with Keras, and the overall machine learning project workflow, all of which are useful for autoencoder preparation.