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Related lectures (28)

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Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Self-Supervised Learning: State of the Art

Explores self-supervised learning, transfer learning, SSL prediction tasks, feature learning, image rotations, contrastive learning, and vision learners.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Transformers in Visual Intelligence

Explores transformers in visual intelligence, focusing on object detection, image synthesis, and feature fusion.

Transformations of Input or Output

Covers handling missing data, feature engineering, and output transformations in machine learning.

Logistic Regression: Classification

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Contrastive losses: Word2Vec and Skip-gram

Covers contrastive losses in Word2Vec and Skip-gram models, negative sampling, Noise Contrastive Estimation, and InfoNCE/CPC.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.