This page is where I am collecting analogies to explain artificial intelligence, machine learning, and all that jazz.

An ananlogy

A tensorflow model is how you plan your hunt. You select the game you're after, study their habits, and choose the right equipment. Then you go out into the field and track your prey. The better you know your quarry, the more likely you are to bring home a trophy.
In the same way, text and labels are used to train a tensorflow model in recognizing malformed sentences. By carefully studying a set of data, the model can learn to identify patterns and make predictions. The more data it has to work with, the more accurate its predictions will be.
the way the model recognizes patterns is analogous to the way a hunter tracks their prey. The more data the model has, the more accurately it can identify patterns and make predictions.
the way the model gets data is analogous to the way a hunter gets information about their prey. By studying a set of data, the model can learn to identify patterns and make predictions. The more data it has to work with, the more accurate its predictions will be.
when a model correctly identifies a sentence as malformed, it is analogous to a hunter successfully tracking and killing their prey because both require a great deal of skill and knowledge and that is why text and labels are used to train a tensorflow model in recognizing malformed sentences.

Another Analogy using lists

The machine learning model is like a hunter going after its prey. It stalks the prey, tracks its every move, and then finally pounces when the time is right. This is how the machine learning model works, it stalks the data, tracks its every move, and then finally pounces when the time is right. the prey is the data and the hunter is the machine learning model. The hunter learns like a machine learning model in the following ways:
1. The hunter learns about its prey through observation.
2. The hunter tracks the prey's every move.
3. The hunter plans its attack and pounces when the time is right.
4. The hunter never gives up and continues to stalk the prey until it is caught.
5. The hunter is always learning and adapting to its environment.
6. The hunter is patient and waits for the perfect opportunity to strike.
the way these are like a machine learning model are
1. the machine learning model learns about data through observation
2. the machine learning model tracks the data's every move
3. the machine learning model plans its attack and pounces when the time is right
4. the machine learning model never gives up and continues to stalk the data until it is caught
5. the machine learning model is always learning and adapting to its environment
6. The machine learning model is patient and waits for the perfect opportunity to strike
the machine learning model strikes by outputting predictions, and predictions are based on the data that was input into the model.

what is back propagation

The process of back propagation is like a hunter tracking their prey. They follow the tracks left behind, and eventually they catch up to the animal. this is an analogy for how back propagation works, following the path of the error until it is eventually corrected. the error is the prey, and the algorithm is the hunter. it is corrected by finding the error and then adjusting the weights accordingly. and it was known there was an error because the output was not what was expected. this is like hunting because the hunter knows there was an animal there, because they found tracks. and they know how to track the animal, because they have experience with it. this is like back propagation, because it knows there was an error, because the output was not what was expected. and it knows how to find the error and correct it, because it has been trained on how to do so.

What is the actor critic training model?


Imagine you are a deer, and the forest is your environment. You have to find food and avoid predators. You are constantly trying to figure out the best way to do this by trial and error.
The hunters are the algorithms that are trying to learn how to best find and capture you. They start off by randomly exploring the forest, trying different methods of hunting. Some of these methods may work better than others, but the hunters don't know which ones are the best until they try them out.
As the hunters keep trying different things, they gradually get better at finding and capturing you. They learn from their mistakes and figure out what works and what doesn't. This is how actor critic training works: the algorithms learn by trial and error, constantly improving as they go along.