Machine Learning - Introduction to Variational Autoencoders

Machine Learning : Introduction to Variational Autoencoders, available at $34.99, has an average rating of 3.8, with 11 lectures, based on 17 reviews, and has 79 subscribers.

You will learn about 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 This course is ideal for individuals who are For those interested in Autoencoders or For those interested in Artificial Intelligence (AI) or For those who want to be ready for the Artificial Intelligence (AI) revolution It is particularly useful for For those interested in Autoencoders or For those interested in Artificial Intelligence (AI) or For those who want to be ready for the Artificial Intelligence (AI) revolution.

Enroll now: Machine Learning : Introduction to Variational Autoencoders

Summary

Title: Machine Learning : Introduction to Variational Autoencoders

Price: $34.99

Average Rating: 3.8

Number of Lectures: 11

Number of Published Lectures: 11

Number of Curriculum Items: 11

Number of Published Curriculum Objects: 11

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 Python

Unsupervised 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Autoencoders: intuitive explanation

Lecture 3: Autoencoders: applications

Chapter 2: Autoencoders

Lecture 1: Encoder and Decoder

Lecture 2: Training algorithm

Lecture 3: Compression

Lecture 4: Amortization

Lecture 5: Latent space exploration

Chapter 3: Variational Autoencoders

Lecture 1: Auto-Encoding Variational Bayes

Lecture 2: VAEs implementation

Chapter 4: Conclusion

Lecture 1: Conclusion

Instructors

Maxime Vandegar
Ingénieur de recherche

Rating Distribution

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2 stars: 2 votes

3 stars: 2 votes

4 stars: 6 votes

5 stars: 7 votes

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