Variance Reduction in Deep Learning

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Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Covers the fundamentals of multilayer neural networks and deep learning.

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Delves into deep learning optimization, challenges, SGD variants, critical points, overparametrized networks, and adaptive methods.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Explores the No Free Lunch Theorem and how deep networks match real-world problem structures.