Lecture
Committee Machine: Statistical Physics Approach
Related lectures (31)
Neural Networks: Multilayer Perceptrons
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Neural Networks: Regression and Classification
Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.
Feature Engineering: Missing Data and Standardization
Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.
Gradient Descent: Optimization Techniques
Explores gradient descent, loss functions, and optimization techniques in neural network training.
Neural Networks: Basics and Applications
Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
Neural Networks: Multilayer Perceptron
Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.
Neural Networks: Random Features and Kernel Regression
Explores random features in neural networks and kernel regression using stochastic gradient descent.
Neural Networks: Multilayer Learning
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.