Enigmatic Autoencoders for Dimensionality Reduction

Overview

In this course, explore how autoencoders can compress and reconstruct data, offering insights into unsupervised learning for dimensionality reduction.

Syllabus

Unit 1: Neural Networks in R

Starship Categorization Neural Network Development
Neural Network Implementation in R Using Keras3
Neural Network Construction from Scratch

Unit 2: Neural Network Forward Propagation

Iris Flower Classification Using Neural Networks
Neural Network Enhancement for Iris Classification
Building a Simple Neural Network for Binary Classification

Unit 3: Autoencoders with R

Autoencoder for 2D Data Compression
Adjusting Autoencoder Activation Functions
Autoencoder for 2D Data Reconstruction

Unit 4: Tuning Autoencoders in R

Autoencoder Learning Rate Impact Analysis
Activation Function Adjustment in Autoencoder
Effect of Learning Rates on Autoencoder Performance

Unit 5: Comparing Autoencoder Optimizers

Comparison of Optimizers in Autoencoder Performance
Autoencoder Optimizer Comparison Task
Training Autoencoders with Various Optimizers

Reviews

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Enigmatic Autoencoders for Dimensionality Reduction

Deep Learning and Reinforcement Learning

Denoising and Variational Autoencoders

Deep Learning Fundamentals with Keras

Non-linear Dimensionality Reduction Techniques

Pre-Training U-Net Using Autoencoders - Autoencoders and Visualizing Features

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