This course covers the learning outcomes and goals of understanding autoencoders as generative models, specifically focusing on denoising autoencoders and variational autoencoders. The individual skills taught include dimensionality reduction, training autoencoders, and working with datasets of images. The teaching method involves a video format with sections on different topics related to autoencoders. The intended audience for this course is individuals interested in deep learning, neural networks, and generative models.
Overview
Syllabus
Intro:
Dimensionality reduction
Denoising autoencoders
Variational autoencoders
Training autoencoders
Introduction
Generative models
Variational autoencoders
Dataset of images
Denoising autoencoders
Linear methods
A friendly introduction to deep learning and neural networks
Mapping the real numbers to the interval 0,1
Sigmoid function
Perceptron
Correct noise
Autoencoders as generators
Latent space
Training a neural network - loss function
Training an autoencoder
Training autoencoders
Reconstruction loss Mean squared error
Reconstruction loss log-loss
Training a variational auto encoder
Taught by
Serrano.Academy
