Deep Learning Modus Operandi

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 1 of 3

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Supervised Learning with kNN: Regression Model

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Deep Generative Models: Variational Autoencoders & GANs

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

Document Analysis: Topic Modeling

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

Learning with Deep Neural Networks

Explores the success and challenges of deep learning, including overfitting, generalization, and the impact on various domains.

Machine Learning Review

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Distributions & Interpolation Spaces

Covers distributions, interpolation spaces, convergence, and the concept of dual spaces.

Compression: Prediction

Covers the concepts of compression and prediction using prefix-free codes and distributions.

Deep Generative Models

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.