Lecture
Variance Reduction in Deep Learning
Related lectures (38)
Double Descent Curves: Overparametrization
Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Deep Learning Fundamentals
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Neural Networks: Training and Optimization
Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.
Optimality of Convergence Rates: Accelerated Gradient Descent
Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.
Neural Networks: Training and Optimization
Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.
Variance Reduction Techniques
Covers variance reduction techniques in optimization, focusing on gradient descent and stochastic gradient descent methods.