Construct and train advanced autoencoder architectures for effective representation learning. This course covers the theoretical foundations and practical implementations of various autoencoders, including sparse, denoising, variational, and adversarial models. Learn to manipulate latent spaces and apply these techniques to dimensionality reduction, anomaly detection, and generative tasks using modern deep learning frameworks.
Prerequisites Python, ML, Deep Learning
Level:
Advanced Autoencoder Architectures
Implement and differentiate between various autoencoder types like DAEs, Sparse AEs, VAEs, AAEs, and Convolutional AEs.
Representation Learning Theory
Understand the principles of representation learning and how autoencoders learn meaningful data compressions.
Latent Space Manipulation
Analyze, visualize, and manipulate latent spaces learned by autoencoders for tasks like generation and disentanglement.
Variational Autoencoders (VAEs)
Grasp the probabilistic foundations of VAEs, including the reparameterization trick and the ELBO objective.
Practical Implementation
Apply autoencoders to tasks such as dimensionality reduction, anomaly detection, and data generation using Python and deep learning libraries.
Model Training and Evaluation
Utilize appropriate loss functions, regularization methods, and evaluation metrics for training robust autoencoder models.
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