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Variational Autoencoders (VAEs)

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May 11, 2024 5 minute read

Image representing Variational Autoencoders (VAEs)

Variational Autoencoders (VAEs) are a powerful generative model in the field of deep learning, capable of learning the underlying distribution of data and generating new samples that resemble the original dataset. This breakthrough has made VAEs a popular choice for various applications, including image and text generation, anomaly detection, and image compression.

Why Learn about Variational Autoencoders (VAEs)?

There are several reasons why individuals may be interested in learning about Variational Autoencoders (VAEs):

Path to Variational Autoencoders (VAEs)

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We've curated three courses to help you on your path to Variational Autoencoders (VAEs). Use these to develop your skills, build background knowledge, and put what you learn to practice.

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Exploring Generative AI Models and Architecture

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Generative Deep Learning with TensorFlow

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Generative AI and LLMs: Architecture and Data Preparation

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Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Variational Autoencoders (VAEs).

Sparse Coding and its Applications in Computer...

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Provides a deep dive into variational deep learning, including VAEs, and their applications. It is written by leading researchers in the field and is suitable for advanced students and researchers.

Sparse Coding And Its Applications In Computer...

Kindle Edition

Deep Learning

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Provides a gentle introduction to VAEs, making them accessible to a broad audience. It is written by a leading researcher in the field and is suitable for beginners and experienced researchers alike.

Deep Learning: Foundations and Concepts

Hardcover

$$$

Applied Statistical Inference

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Provides a statistical perspective on VAEs, making them accessible to a broad audience. It is written by two leading researchers in the field and is suitable for both beginners and experienced researchers.

Applied Statistical Inference: Likelihood and Bayes

Kindle Edition

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Variational Methods in Mathematics, Science and...

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Provides a deep dive into variational methods for stochastic partial differential equations, including VAEs. It is written by three leading researchers in the field and is suitable for advanced students and researchers.

Variational Methods in Mathematics, Science and...

Hardcover

$$$$

Bayesian Analysis of Stochastic Process Models

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Provides a comprehensive overview of approximate Bayesian inference, including VAEs. It is written by two leading researchers in the field and is suitable for advanced students and researchers.

Bayesian Analysis of Stochastic Process Models

Hardcover

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Bayesian Analysis of Stochastic Process Models...

Kindle Edition

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Generative Deep Learning

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Provides a clear and concise explanation of VAEs, making them easy to understand for beginners. It is written by a leading researcher in the field and is suitable for both beginners and experienced researchers.

Generative Deep Learning: Teaching Machines To...

Paperback

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Generative Deep Learning: Teaching Machines to...

Paperback

$$$$

(Deutsch) Generatives Deep Learning: Maschinen das Malen,...

Kindle Edition

(Français) Deep learning génératif

Paperback

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Generative Deep Learning: Teaching Machines To...