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CodeSignal

Enigmatic Autoencoders for Dimensionality Reduction

via CodeSignal

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Overview

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

Syllabus

  • Unit 1: Building Neural Networks with Keras: An Introduction
    • Exploring the Cosmos with Neural Networks
    • Building Your Own Neural Network Spacecraft
    • Crafting a Neural Network with Keras
  • Unit 2: Understanding Forward Propagation in Neural Networks
    • Iris Flower Classification with Neural Networks
    • Adding Hidden and Output Layers and Compiling the Neural Network
    • Building and Training a Neural Network
  • Unit 3: Understanding and Implementing Autoencoders with Keras for Dimensionality Reduction
    • Exploring Autoencoders with Digit Reconstruction
    • Autoencoder Decoder Adjustment
    • Autoencoder Space Odyssey: Compress and Reconstruct
  • Unit 4: Fine-Tuning Autoencoders: Mastering Hyperparameters
    • Observing Autoencoder Performance with Different Learning Rates
    • Autoencoder Activation Function Exploration
    • Creating an Autoencoder with Optimal Learning Rate
  • Unit 5: Understanding Optimizers in Autoencoders
    • Navigating the Cosmos of Optimizers
    • Setting Up the Autoencoder Optimizer
    • Navigating the Cosmos of Optimizers

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