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Applied Autoencoders for Feature Extraction

Chapter 1: Revisiting Neural Networks and Dimensionality Reduction

Neural Network Components: A Quick Review

The Problem of High-Dimensional Data

Overview of Dimensionality Reduction Methods

Contrasting Linear and Non-linear Dimensionality Reduction

Feature Extraction's Role in ML Pipelines

Setting Up Your Deep Learning Environment

Hands-on: PCA for Dimensionality Reduction Practice

Chapter 2: Understanding Autoencoders: Core Concepts

Defining Autoencoders: The Basic Structure

The Encoder: Compressing Information

The Bottleneck Layer: Latent Space Representation

The Decoder: Reconstructing Original Data

Measuring Reconstruction Quality: Loss Functions

Undercomplete and Overcomplete Autoencoders

The Training Process for Autoencoders

How Autoencoders Discover Meaningful Features

Hands-on: Building a Basic Autoencoder

Chapter 3: Building Your First Autoencoder for Feature Extraction

Data Preparation for Autoencoder Training

Encoder Network Design Choices

Determining Latent Space Dimensionality

Decoder Network Design Strategies

Selecting Appropriate Loss Functions for Autoencoders

Optimizer Selection and Learning Rate Configuration

Monitoring Autoencoder Training Progress

Techniques for Extracting Features from the Bottleneck

Visualizing Latent Space (When Applicable)

Hands-on: Feature Extraction from Tabular Data

Chapter 4: Advanced Autoencoder Architectures

Sparse Autoencoders: Inducing Sparsity in Representations

Regularization Methods for Sparse Autoencoders

Denoising Autoencoders: Learning from Noisy Inputs

Implementing Denoising Autoencoders

Contractive Autoencoders: Principles and Regularization

Stacked Autoencoders: Building Deep Architectures

Layer-wise Training for Stacked Autoencoders

Hands-on: Implementing a Denoising Autoencoder

Chapter 5: Convolutional Autoencoders for Image Data

Why Fully-Connected Autoencoders Fall Short for Images

Convolutional Layers in Autoencoder Encoders

Using Pooling Layers for Spatial Down-sampling

Transposed Convolutional Layers in Decoders

Upsampling Techniques in Decoders

Constructing a Convolutional Autoencoder Model

Extracting Hierarchical Features with Conv Autoencoders

Hands-on: Convolutional Autoencoder for Image Features

Chapter 6: Variational Autoencoders (VAEs) for Structured Latent Spaces

Introduction to Generative Modeling with Autoencoders

Principles of Variational Autoencoders

The VAE Encoder: Outputting Distribution Parameters

The Reparameterization Trick Explained

The VAE Decoder: Generating Data from Latent Samples

The VAE Loss Function: Balancing Reconstruction and Regularization

Characteristics of the VAE Latent Space

Using VAE Latent Representations as Features

Hands-on: Building a VAE and Inspecting Its Latent Space

Chapter 7: Applying Autoencoder Features and Practical Guidance

Selecting an Appropriate Autoencoder Type

Tuning Hyperparameters for Optimal Performance

Methods for Evaluating Extracted Feature Quality

Integrating Autoencoder Features into Supervised Models

Application: Anomaly Detection with Autoencoder Features

Application: Data Compression using Autoencoders

Transfer Learning Approaches with Autoencoders

Addressing Common Implementation Challenges

Practice: Using Autoencoder Features in a Classification Task

Applied Autoencoders for Feature Extraction

Prerequisites: Python, basic Machine Learning.

Level: Intermediate

What You'll Learn

Understand Autoencoder Architectures
Grasp the components and variations of autoencoders, including simple, sparse, denoising, convolutional, and variational autoencoders.
Implement Autoencoders
Develop and train autoencoder models using popular deep learning frameworks like TensorFlow or PyTorch.
Extract Meaningful Features
Utilize the latent space of trained autoencoders to generate compressed, informative, and useful features from various data types.
Apply to Diverse Datasets
Apply autoencoder-based feature extraction techniques to different kinds of data, including tabular and image data.
Evaluate Feature Quality
Assess the effectiveness of extracted features by evaluating their impact on the performance of downstream machine learning models.

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