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
Configuring the Model for Training
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Sep 5, 2025
Update to use Pytorch with Keras 3 instead of Tensorflow
- tf.keras.Model.compile, TensorFlow Developers, 2024 - Official documentation detailing the
compilemethod in Keras, which configures a model for training by specifying the optimizer, loss function, and metrics. - Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A textbook offering comprehensive coverage of deep learning, including foundational explanations of loss functions (e.g., MSE, cross-entropy) and optimization algorithms (e.g., SGD, Adam's principles) crucial for model training.
- Adam: A Method for Stochastic Optimization, Diederik P. Kingma and Jimmy Ba, 2014 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1412.6980 - The original research paper introducing the Adam optimizer, a widely adopted optimization algorithm for training deep neural networks due to its efficiency and adaptive learning rate capabilities.