Lectures related to Neural networks under SGD

Covers the analysis of scalar fields, including divergence, gradient, and Laplacian.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Gradient, divergence

Covers the definitions of gradient and divergence, including the Cartesian coordinate system and the divergence theorem.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Double Descent Curves: Overparametrization

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Vector Calculus Review: Maxwell Equations Log in to Mediaspace to watch this video

Covers a review of vector calculus and the Maxwell equations in electromagnetism.

Vector Fields: Gradient and Divergence Log in to Mediaspace to watch this video

Covers vector fields, gradient, divergence, heat flux, and stress tensors.

Neural Networks: Training and Optimization Log in to Mediaspace to watch this video

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Curve Integrals: Gauss/Green Theorem Log in to Mediaspace to watch this video

Explores the application of the Gauss/Green theorem to calculate curve integrals along simple closed curves.

Curve Integrals of Vector Fields Log in to Mediaspace to watch this video

Explores curve integrals of vector fields, emphasizing energy considerations for motion against or with wind, and introduces unit tangent and unit normal vectors.

Surface Integrals: Parameterization and Divergence Theorem Log in to Mediaspace to watch this video

Explores surface integrals using parameterization and the divergence theorem, with practical examples included.

Divergence of Vector Fields Log in to Mediaspace to watch this video

Explores divergence of vector fields, rotational definitions, and integral derivation applications.

Divergence Theorem: Green Identities in R² Log in to Mediaspace to watch this video

Explores the divergence theorem and corollaries related to Green identities in the plane, demonstrating their application through examples.

Vectors and Tensors: Derivatives and Transformations Log in to Mediaspace to watch this video

Explores gradient, divergence, and integral theorems in vector calculus.

Surface Integrals, Divergence Theorem and Stocks' Theorem Log in to Mediaspace to watch this video

Covers surface integrals, the divergence theorem, and Stocks' theorem through examples and analogies.

Differential Operators: Gradient and Divergence Log in to Mediaspace to watch this video

Introduces differential operators, gradient, and divergence in vector fields.

Surface Integrals: Change of Variables Log in to Mediaspace to watch this video

Explores surface integrals, change of variables, and properties of regular surfaces.

Differential Operators: Theorems and Proofs Log in to Mediaspace to watch this video

Covers the concept of differential operators and presents theorems and proofs related to scalar and vector fields.