YoVDO

Unsupervised Learning - Autoencoding the Targets

Offered By: Alfredo Canziani via YouTube

Tags

Autoencoders Courses Deep Learning Courses Unsupervised Learning Courses Super-Resolution Courses Generative Models Courses

Course Description

Overview

Explore unsupervised learning and autoencoding techniques in this comprehensive 57-minute lecture. Delve into topics such as generative models, input and latent space interpolation, conditional generative networks, and style transfer. Learn about super resolution, inpainting, and caption-to-image generation using Dall-e. Understand key concepts like energy-based models, reconstruction energies, and loss functionals. Examine various autoencoder architectures, including denoising, nearest neighborhood, and sparse autoencoders. Gain insights into under and over-complete hidden layers, and conclude with final remarks on the subject.

Syllabus

– 2021 edition disclaimer
– Unsupervised learning and generative models
– Input space interpolation
– Latent space interpolation
– Conditional generative networks
– Style transfer
– Super resolution
– Inpainting
– Caption to image Dall-e
– Definitions: x, y, z
– Recap: conditional latent variable EBM
– Recap: energy function
– Softmin training recap → autoencoder via amortised inference
– Reconstruction energies
– Loss functional
– Under and over complete hidden layer
– Denoising autoencoder
– Nearest neighbourhood denoising autoencoder
– Sparse autoencoder
– Final remarks


Taught by

Alfredo Canziani

Tags

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera
機器學習技法 (Machine Learning Techniques)
National Taiwan University via Coursera
Machine Learning Capstone: An Intelligent Application with Deep Learning
University of Washington via Coursera
Прикладные задачи анализа данных
Moscow Institute of Physics and Technology via Coursera
Leading Ambitious Teaching and Learning
Microsoft via edX