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:
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Autoencoders and their implementation in Python
-
Variational Autoencoders and their implementations in Python
-
Unsupervised Learning
-
Generative models
-
PyTorch through practice
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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
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Maxime Vandegar
Ingénieur de recherche
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 2 votes
- 3 stars: 2 votes
- 4 stars: 6 votes
- 5 stars: 7 votes
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