Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Linear Binary Classification: Perceptron, SGD, Fisher's LDA
Graph Chatbot
Related lectures (29)
Previous
Page 2 of 3
Next
Deep Learning: Multilayer Perceptron and Training
Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Linear Models for Classification: Multi-Class Extensions
Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.
Deep Learning Paradigm
Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.
Gradient Descent: MNIST Dataset and Logistic Loss
Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Logistic Regression: Fundamentals and Applications
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Multiclass Classification
Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Logistic Regression: Statistical Inference and Machine Learning
Covers logistic regression, likelihood function, Newton's method, and classification error estimation.
Multilayer Neural Networks: Deep Learning
Covers the fundamentals of multilayer neural networks and deep learning.
Unsupervised Learning: Dimensionality Reduction
Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.