Все публикации

1:00:59

Introduction to Machine Learning Lecture 7: Gradient Descent

1:09:06

Introduction to Machine Learning Lecture 6: Bayesian Decision THeory

1:12:38

Introduction to Machine Learning Lecture 5: k-means clustering and Gaussian Mixture Models

1:18:25

Introduction to Machine Learning Lecture 4: Density estimation

1:15:38

Introduction to Machine Learning Lecture 3: Curve fitting

1:15:32

Introduction to Machine Learning Lecture 2: Datasets and Ethics

1:08:11

Introduction to Machine Learning Lecture 1: Introduction

0:02:31

Computational Creativity 2023

0:54:46

Computational Creativity Lecture 22: Generative models for X (vector graphics, layouts, animation)

1:01:08

Computational Creativity Lecture 21: Generative models for 3D

1:09:18

Computational Creativity Lecture 20: 3D representations and neural radiance fields (NeRFs)

0:54:58

Computational Creativity Lecture 19: Generative Models for Music

0:59:21

Computational Creativity Lecture 18: Diffusion Developments

0:58:50

Computational Creativity Lecture 16: CLIP and its applications

1:02:11

Computational Creativity Lecture 17: DALL-E 2 and Stable Diffusion

0:58:00

Computational Creativity Lecture 15: Large language models and their implications

1:04:49

Computational Creativity Lecture 14: Attention and transformers

1:04:43

Computational Creativity Lecture 13: Neural language models and word embeddings

0:46:01

Computational Creativity Lecture 12: Normalizing flow models

0:57:04

Computational Creativity Lecture 11: Denoising diffusion models

0:47:49

Computational Creativity Lecture 10: DeepDream and neural style transfer

0:52:12

Computational Creativity Lecture 9: Image-to-Image GANs and GAN artists

1:00:19

Computational Creativity Lecture 8: Advanced GANs

0:56:10

Computational Creativity Lecture 7: Generative Adversarial Networks (GANs)