Machine Learning : Introduction To Variational Autoencoders

Autoencoders and Variational Autoencoders from scratch | Auto-Encoding Variational Bayes paper | Deep Learning | PyTorch

What you'll learn
An intuitive explanation of Autoencoders
Implementing Autoencoders using Python (and PyTorch)
Applications and opportunities offered by (variational) Autoencoders
The paper "Auto-Encoding Variational Bayes"
Exploration of the latent space
Machine Learning and Deep Learning concepts including unsupervised learning and generative modeling
Requirements
Basic programming knowledge
Basic knowledge of machine learning
Description
In a world of increasingly accessible data, unsupervised learning algorithms are becoming more and more efficient and profitable. Companies that understand this will soon have a competitive advantage over those who are slow to jump on the artificial intelligence bandwagon. As a result, developers with Machine Learning and Deep Learning skills are increasingly in demand and have gold on their hands. In this course, we will see how to take advantage of a raw dataset, without any labels. In particular, we will focus exclusively on Autoencoders and Variational Autoencoders and see how they can be trained in an unsupervised way, making them particularly attractive in the era of Big Data. This course, taught using the Python programming language, requires basic programming skills. If you don't have the required foundation, I recommend that you brush up on your skills by taking a crash course in programming. Also, it is best to have basic knowledge of optimization (we will use gradient optimization) and machine learning.Concepts covered: Autoencoders and their implementation in Python Variational Autoencoders and their implementations in PythonUnsupervised Learning Generative models PyTorch through practice The implementation of a scientific ML paper (Auto-Encoding Variational Bayes) Don't wait any longer before jumping into the world of unsupervised Machine Learning!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Autoencoders: intuitive explanation

Lecture 3 Autoencoders: applications

Section 2: Autoencoders

Lecture 4 Encoder and Decoder

Lecture 5 Training algorithm

Lecture 6 Compression

Lecture 7 Amortization

Lecture 8 Latent space exploration

Section 3: Variational Autoencoders

Lecture 9 Auto-Encoding Variational Bayes

Lecture 10 VAEs implementation

Section 4: Conclusion

Lecture 11 Conclusion

For those interested in Autoencoders,For those interested in Artificial Intelligence (AI),For those who want to be ready for the Artificial Intelligence (AI) revolution