prompter: Explain neural-nets like I'm 5

assistant: A neural-net is like a big computer brain that can learn and think like a human brain. Just like our brain has lots of tiny parts called neurons that talk to each other, a neural-net also has lots of tiny parts that work together to solve problems.

Think of a puzzle. You can put the pieces together to make a picture, right? That's like what a neural-net does. It uses lots of pieces of information to solve a problem and make a decision.

And just like you can get better at solving puzzles the more you practice, a neural-net can also get better at solving problems the more it practices. It can learn from its mistakes and get better at making decisions over time.

Isn't that cool? A computer that can think and learn like a person!

prompter: it sure sounds cool, but where do those puzzle pieces come from?

assistant: A neural network is like a big team of many, many friends who help a computer make decisions and solve problems. Each friend, called a neuron, has a special job, like recognizing patterns, making decisions, or solving problems. When the computer has a task, all the friends work together to find the best solution. The number of friends in a neural network can be very large, sometimes even in the millions! This is what makes neural networks so powerful, as they can handle complex tasks that would be difficult for a single person or a smaller group of friends to solve. Just like how you and your friends help each other, a neural network helps the computer learn and make decisions.

prompter: How do you train a neural net?

assistant: Assuming you want an explanation easily comprehended by a five year-old:

"Imagine you have a lot of colored blocks. Each color represents something different, like red for apples and green for bananas.

Now imagine you have a robot that can look at the blocks and sort them into different piles. But sometimes the robot makes mistakes and puts the wrong color block into the wrong pile.

So, you want to teach the robot how to do a better job of sorting the blocks. You show the robot a lot of blocks and tell it which pile to put each one in. The robot starts to remember which color block goes into which pile.

This is like training a neural network. The neural network is the robot, and the blocks are the data. You show the neural network a lot of data and tell it what the correct answer is. Over time, the neural network gets better and better at recognizing the patterns in the data.

Just like how you want the robot to sort the blocks into the right piles, you want the neural network to make the correct predictions about the data."

assistant: To train a neural network, using the friend analogy previously outlined, you would give the first friend in the network an interesting but unknown object or piece of data and ask them to tell the next friend something important about this object. Once the object has made it to the last friend he must use all the bits of information passed down from previous friends to make a guess as to what this object really is! If this guess is correct then everyone in this group of friends accomplished their goal but if he is incorrect then the guesser has to return a correction message down the chain of friends to tell them where they were incorrect about their descriptions. To train a neural net it is usually required to input millions of such unknown objects.

assistant: A neural network is a computer system which tries to be able to accomplish creative and/or intelligent actions. They have to learn, like people and are a network of "neurons" inspired by the human brain.

prompter: How long does it take for a neural-net to learn something new?

assistant: The time it takes for a neural network to learn highly depends on several factors, such as the speed of the computers it is being trained on and the type of the neural network and what it is doing.

assistant: Training a Neural Network takes a varying amount of time based on the complexity of the data provided and the layers the Net has.

assistant: depetens on the hardware and the data are availbale to the system