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
A Glimpse into Overfitting and Underfitting
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This foundational textbook provides a comprehensive theoretical background on deep learning, including detailed explanations of overfitting, underfitting, and generalization concepts for training neural networks like autoencoders.
- CS230: Deep Learning - Lecture Notes on Bias/Variance and Regularization, Andrew Ng, 2018 (DeepLearning.AI) - Lecture notes from Stanford University's Deep Learning course, offering an academic introduction to underfitting and overfitting in neural networks and initial strategies for addressing them.
- Deep Learning with Python, François Chollet, 2021 (Manning Publications) - A practical guide to deep learning with Keras and TensorFlow, offering hands-on explanations of common training issues like overfitting and underfitting, and practical methods to mitigate them.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer) DOI: 10.1007/978-0-387-84858-7 - A canonical textbook on statistical learning, offering a rigorous treatment of model fitting, bias-variance tradeoff, and generalization errors, which form the basis of overfitting and underfitting.