# Table of Contents

# Personal List

## Meta-resources

- Depth First Learning (depth first expositions into papers. covers all material you need to learn topic)

## Normalizing Flows

- Normalizing Flows for Probabilistic Modeling and Inference - recommended by David Duvenaud
- Eric Jangâ€™s tutorial
- Lil Log blog post on Flow Based Deep Generative Models

## Control as Inference/ Inference in RL

Papers

- An inference perspective on model-based reinforcement learning
- Reinforcement learning and control as probabilistic inference

Blog Posts

- Blog Post (by Dibya Ghosh (promising UC Berkeley undergrad))

## Variational Inference

- Spherical VAE(didactic paper on variational autoencoders)
- KL-Divergence Blog Post (by Dibya Ghosh (promising UC Berkeley undergrad))

## Monte Carlo

- MCMC intution for everyone
- Hamiltonian Monte Carlo
- Hamiltonian Monte Carlo explained (endorsed by Princeton Professor)
- Langrangian and Hamiltonian dynamics introduction (by Princeton Professor)

- The beginners guide to Hamiltonian Monte Carlo by PhD Student in Machine Learning

- Hamiltonian Monte Carlo explained (endorsed by Princeton Professor)