Lectures related to Autoencoder

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Dimensionality Reduction: PCA & Autoencoders

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Non-Linear Dimensionality Reduction

Covers non-linear dimensionality reduction techniques using autoencoders, deep autoencoders, and convolutional autoencoders for various applications.

Dimensionality Reduction: PCA and Kernel PCA

Covers PCA, Kernel PCA, and autoencoders for dimensionality reduction in data analysis.

Understanding Autoencoders

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.

Variational Auto-Encoders and NVIB

Explores Variational Auto-Encoders, Bayesian inference, attention-based latent spaces, and the effectiveness of Transformers in language processing.

Neural Networks

Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Generative Models: Boltzmann Machine

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

Encoder for Force and Vision in Manipulation

Explores combining force and vision for efficient manipulation tasks using a lower dimensional representation.

Self-supervised Learning for Autonomous Vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Understanding Generative Models of Protein Sequences

Explores a unified framework for understanding and evaluating generative sequence models of DNA/RNA or Protein, covering topics like coevolution, conservation, and different models such as GREMLIN and BERT.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Singular Value Decomposition: Fundamentals

Covers the fundamentals of Singular Value Decomposition, including properties, applications, and error measurement.

Affective Computing: Theories, Applications, and Predictive Models

Explores emotion theories, applications, and predictive models in affective computing, analyzing NSF funding trends, emotion impact on education and medicine, and emotion detection through physiological signals and visual data.

Deep Generative Models: Variational Autoencoders & GANs Log in to Mediaspace to watch this video

Explores Variational Autoencoders and Generative Adversarial Networks for deep generative modeling.

Deep Generative Models Log in to Mediaspace to watch this video

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Deep Generative Models Log in to Mediaspace to watch this video

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Document Analysis and Topic Modeling Log in to Mediaspace to watch this video

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.