Gradient Descent

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Related lectures (51)

Gradient Descent: Linear Regression Log in to Mediaspace to watch this video

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Non-Convex Optimization: Techniques and Applications Log in to Mediaspace to watch this video

Covers non-convex optimization techniques and their applications in machine learning.

Deep and Convolutional Networks: Generalization and Optimization Log in to Mediaspace to watch this video

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

Faster Gradient Descent: Projected Optimization Techniques Log in to Mediaspace to watch this video

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Proximal Gradient Descent: Optimization Techniques in Machine Learning Log in to Mediaspace to watch this video

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Optimization Techniques: Gradient Descent and Convex Functions Log in to Mediaspace to watch this video

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Lasso and MNIST Basics Log in to Mediaspace to watch this video

Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.

Convolutional Neural Networks: Fundamentals Log in to Mediaspace to watch this video

Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.

Trust region methods: framework & algorithms Log in to Mediaspace to watch this video

Covers trust region methods, focusing on the framework and algorithms.

Deep Learning: Data Representations and Neural Networks Log in to Mediaspace to watch this video

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Untitled Log in to Mediaspace to watch this video

Variance Reduction: Strategies and Applications Log in to Mediaspace to watch this video

Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.

Optimization algorithms Log in to Mediaspace to watch this video

Covers optimization algorithms, focusing on Proximal Gradient Descent and its variations.

Introduction to Optimization Log in to Mediaspace to watch this video

Introduces linear algebra, calculus, and optimization basics in Euclidean spaces, emphasizing the power of optimization as a modeling tool.

Decision Trees and Random Forests: Concepts and Applications Log in to Mediaspace to watch this video

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Approximate Convergence and Optimization Log in to Mediaspace to watch this video

Explores approximate convergence, optimization theorems, and mirror descent in mathematical algorithms.

Optimization Principles Log in to Mediaspace to watch this video

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Linear Programming: Solving LPs Log in to Mediaspace to watch this video

Covers the process of solving Linear Programs (LPs) using the simplex method.

Dynamic Programming: Bellman-Ford and Dijkstra Log in to Mediaspace to watch this video

Explores dynamic programming with Bellman-Ford, Dijkstra, greedy strategies, and activity scheduling problems.

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