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A comprehensive review of variational inference in one concise book.  @TheVariational
The Variational 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 distributions
  • Score Matching
  • Diffusion
  • Reinforcement learning
Build foundation for further learning by sparking interest in specific areas
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Benefit 3
Acquire knowledge of core concepts with in-depth explanations
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Benefit 1
Who should read this book?
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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.
  • 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
Sign up for our mailing list to get access to free chapters.
About the author
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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 developing various biomedical projects. At the moment, my professional endeavors center around machine learning, primarily on generative design for drug discovery. In my spare time, I enjoy reading  computer science with a keen eye on variational, deep learning and bayesian ideas. 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 early 2024.
Everything you need to know in one concise book, it covers all  variational 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
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