Lectures related to Encoder for Force and Vision in Manipulation

Covers the concepts of work and power, exploring real-world examples and magnitudes of power.

Explores Newton's laws of motion, inertia, force, momentum, and action-reaction principle, with practical examples.

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

Covers Newton's first and second laws, inertia, force, momentum, and action-reaction principle in mechanics.

Explores conservative forces, work, and potential energy calculations.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

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

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