# 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

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

Scroll bar
https://shaharkadmiel.github.io/Sticky-TOC-Sidebar/

(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
Didnâ€™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.
```