Generative Models: Self-Attention and Transformers

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Regression: High Dimensions

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Machine Learning Basics

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

Linear Regression Basics

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Polynomial Regression: Basics and Regularization

Covers the basics of polynomial regression and regularization to prevent overfitting.

Document Analysis and Topic Modeling

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Deep Generative Models: Variational Autoencoders & GANs

Explores Variational Autoencoders and Generative Adversarial Networks for deep generative modeling.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Linear Regression: Basics and Applications

Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Page 2 of 2