The Variational Inference Book

The topic of Variational Inference unites key concepts in machine learning and provides a common framework for probabilistic modeling and inference. Most state-of-the art artificial intelligence algorithms leverage ideas from the topic.

Develop world-class foundational machine learning expertise

Everything in one concise, explanatory book.

Bayesian inference
Model selection and comparison
Exponential distributions
KL-divergence
Mean-Field approximations
Stochastic Variational Inference
Amortized inference
Monte-Carlo approximations and variance reduction

Topics covered include:

Structured approximations
Normalizing Flows
Auxillary distributions
Implicit (Energy) approximation
Score Matching
Diffusion
Reinforcement learning

Build foundation for further learning by sparking interest in specific areas

Benefit 3

Acquire knowledge of core concepts with in-depth explanations

Benefit 1

Who should read this book?

Benefit 2

Undergraduate or graduate students studying computer science, data science, machine learning or STEM.
ML scientists, data scientists or software engineers interested in acquiring advanced expertise in machine learning (understanding theory helps with learning code implementation).
Managers poised to collaborate closely with AI engineers.
Curious minds aspiring to learn how algorithms work and their underlying mathematical foundations.
Self-learner go-getters craving to master what machine learning is all about.

Refine problem-solving skills with step by step derivations

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About the author

Yuri Plotkin

Hi there! My name is Yuri. I'm a machine learning scientist currently living in Los Angeles. I received both my degrees in Biomedical Engineering and have spent time in the wet-lab. At the moment, my professional endeavors center around machine learning, primarily on generative AI. In my spare time, I enjoy reading computer science with a keen eye on variational, Bayesian, diffusion, and llm topics. I hope you enjoy reading the book as much as I have enjoyed writing it.

Feel free to reach me at Twitter and Email

FAQ

When will the book be available?

How does this book set itself apart from existing ML books?

The book release is planned for winter 2024.

Everything you need to know in one concise book, it covers all generative AI techniques used in modern machine learning algorithms. Read the book to grasp the mathematical principles and intuition required for being adept at all things AI.

What if I have a question?

Send an email to author@thevariationalbook.com