# Machine learning and energy project checklist

In general, if a rules based system can solve a problem, it is probably the way to go.

Data challenge

• data flows

maybe a separate post

should applications go further up?

• tense=present & together (let us learn etc)
• check lessons ML, lessons energy
• examples for EVERYTHING

core message =

suprises = not the solution to everything

keep stats on blog posts

## world models post todo

(using the gym env.action_space.sample() or as a Brownian motion (see here). The Brownian motion action sampling is the default. - MISSING A )

• a small helper utility is given in worldmwodels/utils.py:

v2 (next redraft) - vae contributions vae forward pass section use the first paragraphs of each section in the short post medium post = how I spent 3k reimplementing a paper TOC - table contents marcus Aurelius pic link tf record files to the data section more Didn’t use spot at all! Crashing of controller training tree of project in every section?

what would the cost for a perfectly execuned project (minimum bound) = 200 generations + one vae + one memory

The statistics parameterized by the encoder are used to form a distribution over the latent space - a diagonal Gaussian.

This diagonal Gaussian is a multivariate Gaussian with a diagonal covariance matrix - meaning that each variable is independent.

(is this enforcing a gaussian prior or posterior?)

This parameterized Gaussian is an approximation - using it will limit how expressive our latent space is.

$$z \sim P(z \mid x)$$

$$z \mid x \approx \mathbf{N} \Big(\mu_{\theta}, \sigma_{\theta}\Big)$$

We can sample from this latent space distribution, making the encoding of an image $x$ stochastic.