Generative Models
Explore denoising autoencoders, VAEs, GANs, and RBMs in this comprehensive introduction to generative models, covering key concepts and applications in machine learning.
Unsupervised Learning
Explore key unsupervised learning techniques including clustering, dimensionality reduction, and generative models. Gain insights into real-world applications like recommendation systems and image compression.
Neural Networks
This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.
Restricted Boltzmann Machines
Explore Restricted Boltzmann Machines: their layered structure, hidden and visible layers, statistical independence, and applications in modeling high-dimensional data like images.
Restricted Boltzmann Machines for Maximum Satisfiability - IPAM at UCLA
Explore how Restricted Boltzmann Machines can be applied to Maximum Satisfiability problems, leveraging neural network accelerators for efficient stochastic search in combinatorial optimization.
Learning of Neural Networks with Quantum Computers and Learning of Quantum States with Graphical Models
Explores quantum algorithms for neural network learning and quantum state learning using graphical models, showcasing advancements in computational efficiency and sample complexity for specific quantum states.
Learning Quantum with Generative Models
Explore generative models for quantum state learning, covering neural networks, RBMs, and applications to GHZ states and local Hamiltonians.
Restricted Boltzmann Machines - A Friendly Introduction
Friendly introduction to Restricted Boltzmann Machines using real-life examples. Covers key concepts like probabilities, training, contrastive divergence, and Gibbs sampling for machine learning enthusiasts.
Boltzmann Machines - The Grandfather of Generative Models
Explore Boltzmann Machines, early generative models learning data probability distributions using stochastic rules and latent representations. Understand their goals, distribution, update rules, and evolution to Restricted Boltzmann Machines.