Clouds are made of water. When water is vaporized (from boiling it or from evaporation) it goes into the sky as a gas. If there is enough water in the sky, other things in the sky (like dust), will cause the water to condense, or stick together. This accumulation of droplets/crystals is lighter than air and so it floats, making the cloud, and once it gets too heavy, it falls from the sky as rain or snow.

Regression is a somewhat cryptic name coined by a guy in the late 1800s. Basically it's referring to the fact if you have enough data, it will eventually head back towards the mean. You can probably google it to learn more because I'm not an authority figure lol.

Yep, the line is just an estimation of what a dog of that size would probably eat based on the data with the X axis being weight and Y being the predicted food eaten. It would not be perfect because no dog is the same but it would be a pretty good guess with enough data.

Unfortunately I have no help for the deep learning part, but I'll give a go on linear regression:
Linear regression is using one or more variables to predict a response. The way that it is predicted is with a line (hence the linear name). For example, if you wanted to predict how much food your dog ate, you could use linear regression. Let's say that the response is the total amount of food that your dog eats in a week, and the variable is how much the dog weighs. Knowing that a bigger dog should eat more, there will be a trend towards higher values as the dog weighs more. If you pooled 100 pet owners and asked the weight of their dog and how much the dog eats, you can put all of that data together and use a regression model to predict how much a dog of a specific weight might eat. The data can produce a line of best fit with modeling technology which basically makes the best line that minimizes the differences between the sample data and the predicted data (the line). You can also have more variables than one, such as the dog's eating habits (more or less meals a day), how much the dog exercises, or even the breed (which would be categorical, not numerical).

TL;DR: a linear regression model predicts one value based on another value and then fits a line to it that predicts the value as accurately as it can with the data given.

Hemoglobin carries oxygen to your body and when its low it makes the heart work harder. That can cause irregular heart beat which can further lead to things like heart failure.
ELI5: Not enough oxygen kills you.

To add onto this. The cpu could be the gpu as well. It would just be painfully slow. It's called "software rendering"

Edit: The cpu doing software rendering would not technically be a gpu in name, but it's acting as a really slow one.