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 Learning Process: Optimization Basics
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Provides a comprehensive introduction to optimization algorithms, including gradient descent, learning rates, and advanced optimizers, foundational to neural network training.
- Adam: A Method for Stochastic Optimization, Diederik P. Kingma and Jimmy Ba, 2015 3rd International Conference for Learning Representations DOI: 10.48550/arXiv.1412.6980 - Introduces the Adam optimizer, a widely used adaptive learning rate optimization algorithm mentioned in the section as a good default choice for training neural networks.
- CS231n: Deep Learning for Computer Vision Course Notes, Fei-Fei Li, Yunzhu Li, Ruohan Gao, 2023 (Stanford University) - Offers detailed explanations of optimization algorithms, including gradient descent, learning rates, and various optimizers, within the context of training neural networks, with practical examples.