Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Gradient Descent: Optimization Techniques
Graph Chatbot
Related lectures (49)
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.
Cross-Validation: Techniques and Applications
Log in to Mediaspace to watch this video
Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.
Neural Networks: Training and Optimization
Log in to Mediaspace to watch this video
Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.
Feed-forward Networks
Log in to Mediaspace to watch this video
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Neural Networks: Random Features and Kernel Regression
Log in to Mediaspace to watch this video
Explores random features in neural networks and kernel regression using stochastic gradient descent.
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.
Linear Models for Classification: Logistic Regression and SVM
Log in to Mediaspace to watch this video
Covers linear models for classification, focusing on logistic regression and support vector machines.
Deep Learning: Convolutional Neural Networks and Training Techniques
Log in to Mediaspace to watch this video
Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.
Deep Learning: Designing Neural Network Models
Log in to Mediaspace to watch this video
Covers the design and optimization of neural network models in deep learning.
Linear and Logistic Regression
Log in to Mediaspace to watch this video
Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Regression Methods: Model Building and Inference
Log in to Mediaspace to watch this video
Covers analysis of variance, model building, variable selection, and function estimation in regression methods.
Neural Tangent Kernel: Generalization in Deep Learning
Log in to Mediaspace to watch this video
Explores the neural tangent kernel in deep learning, analyzing generalization and network behavior.
Network Simulation and Activity Dynamics
Log in to Mediaspace to watch this video
Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.
Variational Principle in Quantum Mechanics
Log in to Mediaspace to watch this video
Delves into the variational principle in quantum mechanics and the application of deep neural networks for complex system solutions.
KKT Conditions: Convex Optimization
Log in to Mediaspace to watch this video
Explores KKT conditions in convex optimization, covering dual cones, properties, generalized inequalities, and optimality conditions.
Solving Integer Linear Programs
Log in to Mediaspace to watch this video
Covers solving integer linear programs graphically, algorithmically, and through optimization methods.
Optimal Control: Hydropower Management
Log in to Mediaspace to watch this video
Covers the optimization of hydropower systems and control strategies for maximizing energy production.
Unsupervised Learning: Dimensionality Reduction and Clustering
Log in to Mediaspace to watch this video
Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.
Generalization Error in Learning with Random Features
Log in to Mediaspace to watch this video
Explores generalization error in learning theory, emphasizing structured data and the replica method.
Energy Integration Optimization
Log in to Mediaspace to watch this video
Explores tools for optimizing energy integration and minimizing exergy losses in systems, aiding in reducing energy costs and enhancing efficiency.
Previous
Page 2 of 3
Next