In this course, explore how autoencoders can compress and reconstruct data, offering insights into unsupervised learning for dimensionality reduction.
Overview
Syllabus
- Lesson 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
- Lesson 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
- Lesson 3: Understanding and Implementing Autoencoders with Keras for Dimensionality Reduction
- Exploring Autoencoders with Digit Reconstruction
- Autoencoder Decoder Adjustment
- Autoencoder Space Odyssey: Compress and Reconstruct
- Lesson 4: Fine-Tuning Autoencoders: Mastering Hyperparameters
- Observing Autoencoder Performance with Different Learning Rates
- Autoencoder Activation Function Exploration
- Creating an Autoencoder with Optimal Learning Rate
- Lesson 5: Understanding Optimizers in Autoencoders
- Navigating the Cosmos of Optimizers
- Setting Up the Autoencoder Optimizer
- Navigating the Cosmos of Optimizers
